@article{sharma_guinness_muyskens_polizzotto_fuentes_hesterberg_2022, title={<p>Spatial statistical modeling of arsenic accumulation in microsites of diverse soils</p>}, volume={411}, ISSN={["1872-6259"]}, DOI={10.1016/j.geoderma.2022.115697}, abstractNote={Determining reaction mechanisms that control the mobility of nutrients and toxic elements in soil matrices is confounded by complex assemblages of minerals, non-crystalline solids, organic matter, and biota. Our objective was to infer the chemical elements and solids that contribute to As binding in matrices of soil samples from different pedogenic environments at the micrometer spatial scale. Arsenic was reacted with and imaged in thin weathering coatings on eight quartz sand grains separated from soils of different drainage classes to vary contents of Fe and Al (hydr)oxides, organic carbon (OC), and other elements. The grains were analyzed using X-ray fluorescence microprobe (µ-XRF) imaging and microscale X-ray absorption near edge structure (μ-XANES) spectroscopy before and after treatment with 0.1 mM As(V) solution. Partial correlation analyses and regression models developed from multi-element µ-XRF signals collected across 100 × 100 µm2 areas of sand-grain coatings inferred augmenting effects of Fe, Zn, Ti, Mn, or Cu on As retention. Significant partial correlations (r′ > 0.11) between Fe and Al from time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis of most samples suggested that Fe and Al (hydr)oxides were partially co-localized at the microscale. Linear combination fitting (LCF) results for As K-edge μ-XANES spectra collected across grain coatings typically included >80% of As(V) adsorbed on goethite, along with varying proportions of standards of As(V) adsorbed on boehmite, As(V) or As(III) bound to Fe(III)-treated peat, and dimethylarsinic acid. Complementary fits for Fe K-edge μ-XANES spectra included ≥50% of the Fe(III)-treated peat standard for all samples, along with goethite. Our collective results inferred a dominance of Fe and possibly Al (hydr)oxides in controlling As immobilization, with variable contributions from Zn, Ti, Cu, or Mn, both across the coating of a single sand grain and between grains from soils developed under different pedogenic environments. Overall, these results highlight the extreme heterogeneity of soils on the microscale and have implications on soil management for mitigating the adverse environmental impacts of As.}, journal={GEODERMA}, author={Sharma, Aakriti and Guinness, Joseph and Muyskens, Amanda and Polizzotto, Matthew L. and Fuentes, Montserrat and Hesterberg, Dean}, year={2022}, month={Apr} }
 @article{muyskens_guinness_fuentes_2022, title={Partition-Bas'd Nonstationary Covariance Estimation Using the Stochastic Score Approximation}, ISSN={["1537-2715"]}, DOI={10.1080/10618600.2022.2044830}, abstractNote={Abstract We introduce computational methods that allow for effective estimation of a flexible nonstationary spatial model when the field size is too large to compute the multivariate normal likelihood directly. In this method, the field is defined as a weighted spatially varying linear combination of a globally stationary process and locally stationary processes. Often in such a model, the difficulty in its practical use is in the definition of the boundaries for the local processes, and therefore, we describe one such selection procedure that generally captures complex nonstationary relationships. We generalize the use of a stochastic approximation to the score equations in this nonstationary case and provide tools for evaluating the approximate score in operations and O(n) storage for data on a subset of a grid. We perform various simulations to explore the effectiveness and speed of the proposed methods and conclude by predicting average daily temperature. Supplementary materials for this article are available online.}, journal={JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS}, author={Muyskens, Amanda and Guinness, Joseph and Fuentes, Montserrat}, year={2022}, month={Apr} }
 @article{jhuang_fuentes_bandyopadhyay_reich_2020, title={Spatiotemporal signal detection using continuous shrinkage priors}, volume={39}, ISSN={["1097-0258"]}, DOI={10.1002/sim.8514}, abstractNote={Periodontal disease (PD) is a chronic inflammatory disease that affects the gum tissue and bone supporting the teeth. Although tooth‐site level PD progression is believed to be spatio‐temporally referenced, the whole‐mouth average periodontal pocket depth (PPD) has been commonly used as an indicator of the current/active status of PD. This leads to imminent loss of information, and imprecise parameter estimates. Despite availability of statistical methods that accommodates spatiotemporal information for responses collected at the tooth‐site level, the enormity of longitudinal databases derived from oral health practice‐based settings render them unscalable for application. To mitigate this, we introduce a Bayesian spatiotemporal model to detect problematic/diseased tooth‐sites dynamically inside the mouth for any subject obtained from large databases. This is achieved via a spatial continuous sparsity‐inducing shrinkage prior on spatially varying linear‐trend regression coefficients. A low‐rank representation captures the nonstationary covariance structure of the PPD outcomes, and facilitates the relevant Markov chain Monte Carlo computing steps applicable to thousands of study subjects. Application of our method to both simulated data and to a rich database of electronic dental records from the HealthPartners Institute reveal improved prediction performances, compared with alternative models with usual Gaussian priors for regression parameters and conditionally autoregressive specification of the covariance structure.}, number={13}, journal={STATISTICS IN MEDICINE}, author={Jhuang, An-Ting and Fuentes, Montserrat and Bandyopadhyay, Dipankar and Reich, Brian J.}, year={2020}, month={Jun}, pages={1817–1832} }
 @article{rekabdarkolaee_krut_fuentes_reich_2019, title={A Bayesian multivariate functional model with spatially varying coefficient approach for modeling hurricane track data}, volume={29}, ISSN={["2211-6753"]}, DOI={10.1016/j.spasta.2018.12.006}, abstractNote={Abstract Hurricanes are massive storm systems with enormous destructive capabilities. Understanding the trends across space and time of a hurricane track and intensity leads to improved forecasts and minimizes their damage. Viewing the hurricane’s latitude, longitude, and wind speed as functions of time, we propose a novel spatiotemporal multivariate functional model to simultaneously allow for multivariate, longitudinal, and spatially observed data with noisy functional covariates. The proposed procedure is fully Bayesian and inference is performed using MCMC. This new approach is illustrated through simulation studies and analyzing the hurricane track data from 2004 to 2013 in the Atlantic basin. Simulation results indicate that our proposed model offers a significant reduction in the mean square error and averaged interval and increases the coverage probability. In addition, our method offers a 10% reduction in location and wind speed prediction error.}, journal={SPATIAL STATISTICS}, author={Rekabdarkolaee, Hossein Moradi and Krut, Christopher and Fuentes, Montserrat and Reich, Brian J.}, year={2019}, month={Mar}, pages={351–365} }
 @article{huang_reich_fuentes_sankarasubramanian_2019, title={Complete spatial model calibration}, volume={13}, ISSN={1932-6157}, url={http://dx.doi.org/10.1214/18-aoas1219}, DOI={10.1214/18-AOAS1219}, abstractNote={Computer simulation models are central to environmental science. These mathematical models are used to understand complex weather and climate patterns and to predict the climate’s response to different forcings. Climate models are of course not perfect reflections of reality, and so comparison with observed data is needed to quantify and to correct for biases and other deficiencies. We propose a new method to calibrate model output using observed data. Our approach not only matches the marginal distributions of the model output and gridded observed data, but it simultaneously postprocesses the model output to have the same spatial correlation as the observed data. This comprehensive calibration method permits realistic spatial simulations for regional impact studies. We apply the proposed method to global climate model output in North America and show that it successfully calibrates the model output for temperature and precipitation.}, number={2}, journal={The Annals of Applied Statistics}, publisher={Institute of Mathematical Statistics}, author={Huang, Yen-Ning and Reich, Brian J. and Fuentes, Montserrat and Sankarasubramanian, A.}, year={2019}, month={Jun}, pages={746–766} }
 @article{sharma_muyskens_guinness_polizzotto_fuentes_tappero_chen-wiegart_thieme_williams_acerbo_et al._2019, title={Multi-element effects on arsenate accumulation in a geochemical matrix determined using mu-XRF, mu-XANES and spatial statistics}, volume={26}, ISSN={["1600-5775"]}, DOI={10.1107/S1600577519012785}, abstractNote={Soils regulate the environmental impacts of trace elements, but direct measurements of reaction mechanisms in these complex, multi-component systems can be challenging. The objective of this work was to develop approaches for assessing effects of co-localized geochemical matrix elements on the accumulation and chemical speciation of arsenate applied to a soil matrix. Synchrotron X-ray fluorescence microprobe (µ-XRF) images collected across 100 µm × 100 µm and 10 µm × 10 µm regions of a naturally weathered soil sand-grain coating before and after treatment with As(V) solution showed strong positive partial correlations (r′ = 0.77 and 0.64, respectively) between accumulated As and soil Fe, with weaker partial correlations (r′ > 0.1) between As and Ca, and As and Zn in the larger image. Spatial and non-spatial regression models revealed a dominant contribution of Fe and minor contributions of Ca and Ti in predicting accumulated As, depending on the size of the sample area analyzed. Time-of-flight secondary ion mass spectrometry analysis of an area of the sand grain showed a significant correlation (r = 0.51) between Fe and Al, so effects of Fe versus Al (hydr)oxides on accumulated As could not be separated. Fitting results from 25 As K-edge microscale X-ray absorption near-edge structure (µ-XANES) spectra collected across a separate 10 µm × 10 µm region showed ∼60% variation in proportions of Fe(III) and Al(III)-bound As(V) standards, and fits to µ-XANES spectra collected across the 100 µm × 100 µm region were more variable. Consistent with insights from studies on model systems, the results obtained here indicate a dominance of Fe and possibly Al (hydr)oxides in controlling As(V) accumulation within microsites of the soil matrix analyzed, but the analyses inferred minor augmentation from co-localized Ti, Ca and possibly Zn.}, journal={JOURNAL OF SYNCHROTRON RADIATION}, author={Sharma, Aakriti and Muyskens, Amanda and Guinness, Joseph and Polizzotto, Matthew L. and Fuentes, Montserrat and Tappero, Ryan V. and Chen-Wiegart, Yu-chen K. and Thieme, Juergen and Williams, Garth J. and Acerbo, Alvin S. and et al.}, year={2019}, month={Nov}, pages={1967–1979} }
 @article{jhuang_fuentes_jones_esteves_fancher_furman_reich_2019, title={Spatial Signal Detection Using Continuous Shrinkage Priors}, volume={61}, ISSN={["1537-2723"]}, DOI={10.1080/00401706.2018.1546622}, abstractNote={Abstract Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. We apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.}, number={4}, journal={TECHNOMETRICS}, author={Jhuang, An-Ting and Fuentes, Montserrat and Jones, Jacob L. and Esteves, Giovanni and Fancher, Chris M. and Furman, Marschall and Reich, Brian J.}, year={2019}, month={Oct}, pages={494–506} }
 @article{terres_fuentes_hesterberg_polizzotto_2018, title={Bayesian Spectral Modeling for Multivariate Spatial Distributions of Elemental Concentrations in Soil}, volume={13}, ISSN={["1936-0975"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85039854529&partnerID=MN8TOARS}, DOI={10.1214/16-ba1034}, abstractNote={Recent technological advances have enabled researchers in a variety of fields to collect accurately geocoded data for several variables simultaneously. In many cases it may be most appropriate to jointly model these multivariate spatial processes without constraints on their conditional relationships. When data have been collected on a regular lattice, the multivariate conditionally autoregressive (MCAR) models are a common choice. However, inference from these MCARmodels relies heavily on the pre-specified neighborhood structure and often assumes a separable covariance structure. Here, we present a multivariate spatial model using a spectral analysis approach that enables inference on the conditional relationships between the variables that does not rely on a pre-specified neighborhood structure, is non-separable, and is computationally efficient. Covariance and crosscovariance functions are defined in the spectral domain to obtain computational efficiency. The resulting pseudo posterior inference on the correlation matrix allows for quantification of the conditional dependencies. A comparison is made with an MCAR model that is shown to be highly sensitive to the choice of neighborhood. The approaches are illustrated for the toxic element arsenic and four other soil elements whose relative concentrations were measured on a microscale spatial lattice. Understanding conditional relationships between arsenic and other soil elements provides insights for mitigating pervasive arsenic poisoning in drinking water in southern Asia and elsewhere.}, number={1}, journal={BAYESIAN ANALYSIS}, author={Terres, Maria A. and Fuentes, Montserrat and Hesterberg, Dean and Polizzotto, Matthew}, year={2018}, month={Mar}, pages={1–28} }
 @article{cunha_gamerman_fuentes_paez_2017, title={A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro}, volume={66}, ISSN={["1467-9876"]}, DOI={10.1111/rssc.12207}, abstractNote={Summary}, number={5}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS}, author={Cunha, Marcelo and Gamerman, Dani and Fuentes, Montserrat and Paez, Marina}, year={2017}, month={Nov}, pages={919–939} }
 @article{miao_noormets_domec_fuentes_trettin_sun_mcnulty_king_2017, title={Hydrology and microtopography control carbon dynamics in wetlands: Implications in partitioning ecosystem respiration in a coastal plain forested wetland}, volume={247}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2017.08.022}, abstractNote={Wetlands store a disproportionately large fraction of organic carbon relative to their areal coverage, and thus play an important role in global climate mitigation. As destabilization of these stores through land use or environmental change represents a significant climate feedback, it is important to understand the functional regulation of respiratory processes that catabolize them. In this study, we established an eddy covariance flux tower project in a coastal plain forested wetland in North Carolina, USA, and measured total ecosystem respiration (Re) over three years (2009–2011). We evaluated the magnitude and variability of three respiration components – belowground (Rs), coarse woody debris (RCWD), and aboveground plant (Ragp) respiration at the ecosystem scale, by accounting microtopographic variation for upscaling and constraining the mass balance with Re. Strong hydrologic control was detected for Rs and RCWD, whereas Ragp and Re were relatively insensitive to water table fluctuations. In a relatively dry year (2010), this forested wetland respired a total of about 2000 g CO2-C m-2 y-1 annually, 51% as Rs, 37% as Ragp, and 12% as RCWD. During non-flooded periods Rs contributed up to 57% of Re and during flooded periods Ragp contributed up to 69%. The contribution of Rs to Re increased by 2.4% for every cm of decrease in water level at intermediate water table level, and was nearly constant when flooded or when the water level more than 15 cm below ground. The contrasting sensitivity of different respiration components highlights the need for explicit consideration of this dynamic in ecosystem and Earth System Models.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, author={Miao, Guofang and Noormets, Asko and Domec, Jean-Christophe and Fuentes, Montserrat and Trettin, Carl C. and Sun, Ge and McNulty, Steve G. and King, John S.}, year={2017}, month={Dec}, pages={343–355} }
 @article{warren_stingone_herring_luben_fuentes_aylsworth_langlois_botto_correa_olshan_2016, title={Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects}, volume={35}, ISSN={["1097-0258"]}, DOI={10.1002/sim.6891}, abstractNote={Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post‐conception gestational weeks 2–8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post‐conception gestational weeks 2–8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post‐conception gestational weeks 2–8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post‐conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.}, number={16}, journal={STATISTICS IN MEDICINE}, author={Warren, Joshua L. and Stingone, Jeanette A. and Herring, Amy H. and Luben, Thomas J. and Fuentes, Montserrat and Aylsworth, Arthur S. and Langlois, Peter H. and Botto, Lorenzo D. and Correa, Adolfo and Olshan, Andrew F.}, year={2016}, month={Jul}, pages={2786–2801} }
 @article{mannshardt_sucic_fuentes_bingham_2016, title={Comparison of Distributional Statistics of Aquarius and Argo Sea Surface Salinity Measurements}, volume={33}, ISSN={["1520-0426"]}, DOI={10.1175/jtech-d-15-0068.1}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, author={Mannshardt, Elizabeth and Sucic, Katarina and Fuentes, Montserrat and Bingham, Frederick M.}, year={2016}, month={Jan}, pages={103–118} }
 @article{sun_wang_fuentes_2016, title={Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation}, volume={58}, ISSN={["1537-2723"]}, DOI={10.1080/00401706.2015.1017115}, abstractNote={Quantile functions are important in characterizing the entire probability distribution of a random variable, especially when the tail of a skewed distribution is of interest. This article introduces new quantile function estimators for spatial and temporal data with a fused adaptive Lasso penalty to accommodate the dependence in space and time. This method penalizes the difference among neighboring quantiles, hence it is desirable for applications with features ordered in time or space without replicated observations. The theoretical properties are investigated and the performances of the proposed methods are evaluated by simulations. The proposed method is applied to particulate matter (PM) data from the Community Multiscale Air Quality (CMAQ) model to characterize the upper quantiles, which are crucial for studying spatial association between PM concentrations and adverse human health effects.}, number={1}, journal={TECHNOMETRICS}, author={Sun, Ying and Wang, Huixia J. and Fuentes, Montserrat}, year={2016}, month={Jan}, pages={127–137} }
 @article{shivkumar_wang-li_shah_stikeleather_fuentes_2016, title={Performance analysis of a poultry engineering chamber complex for animal environment, air quality, and welfare studies}, volume={59}, number={5}, journal={Transactions of the ASABE}, author={Shivkumar, A. P. and Wang-Li, L. and Shah, S. B. and Stikeleather, L. F. and Fuentes, M.}, year={2016}, pages={1371–1382} }
 @article{warren_fuentes_herring_langlois_2016, title={Spatiotemporal modeling of preterm birth}, journal={Handbook of spatial epidemiology}, author={Warren, J. L. and Fuentes, M. and Herring, A. H. and Langlois, P. H.}, year={2016}, pages={649–663} }
 @article{guinness_fuentes_2015, title={Likelihood approximations for big nonstationary spatial temporal lattice data}, volume={25}, number={1}, journal={Statistica Sinica}, author={Guinness, J. and Fuentes, M.}, year={2015}, pages={329–349} }
 @article{smith_reich_herring_langlois_fuentes_2015, title={Multilevel quantile function modeling with application to birth outcomes}, volume={71}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12294}, abstractNote={Summary}, number={2}, journal={BIOMETRICS}, author={Smith, Luke B. and Reich, Brian J. and Herring, Amy H. and Langlois, Peter H. and Fuentes, Montserrat}, year={2015}, month={Jun}, pages={508–519} }
 @article{smith_fuentes_gordon-larsen_reich_2015, title={QUANTILE REGRESSION FOR MIXED MODELS WITH AN APPLICATION TO EXAMINE BLOOD PRESSURE TRENDS IN CHINA}, volume={9}, ISSN={["1941-7330"]}, DOI={10.1214/15-aoas841}, abstractNote={Cardiometabolic diseases have substantially increased in China in the past 20 years and blood pressure is a primary modifiable risk factor. Using data from the China Health and Nutrition Survey we examine blood pressure trends in China from 1991 to 2009, with a concentration on age cohorts and urbanicity. Very large values of blood pressure are of interest, so we model the conditional quantile functions of systolic and diastolic blood pressure. This allows the covariate effects in the middle of the distribution to vary from those in the upper tail, the focal point of our analysis. We join the distributions of systolic and diastolic blood pressure using a copula, which permits the relationships between the covariates and the two responses to share information and enables probabilistic statements about systolic and diastolic blood pressure jointly. Our copula maintains the marginal distributions of the group quantile effects while accounting for within-subject dependence, enabling inference at the population and subject levels. Our population level regression effects change across quantile level, year, and blood pressure type, providing a rich environment for inference. To our knowledge, this is the first quantile function model to explicitly model within-subject autocorrelation and is the first quantile function approach that simultaneously models multivariate conditional response. We find that the association between high blood pressure and living in an urban area has evolved from positive to negative, with the strongest changes occurring in the upper tail. The increase in urbanization over the last twenty years coupled with the transition from the positive association between urbanization and blood pressure in earlier years to a more uniform association with urbanization suggests increasing blood pressure over time throughout China, even in less urbanized areas. Our methods are available in the R package BSquare.}, number={3}, journal={ANNALS OF APPLIED STATISTICS}, author={Smith, Luke B. and Fuentes, Montserrat and Gordon-Larsen, Penny and Reich, Brian J.}, year={2015}, month={Sep}, pages={1226–1246} }
 @article{reich_fuentes_2015, title={Spatial Bayesian Nonparametric Methods}, ISBN={["978-3-319-19517-9"]}, DOI={10.1007/978-3-319-19518-6_17}, journal={NONPARAMETRIC BAYESIAN INFERENCE IN BIOSTATISTICS}, author={Reich, Brian James and Fuentes, Montserrat}, year={2015}, pages={347–357} }
 @article{vock_reich_fuentes_dominici_2015, title={Spatial Variable Selection Methods for Investigating Acute Health Effects of Fine Particulate Matter Components}, volume={71}, ISSN={["1541-0420"]}, DOI={10.1111/biom.12254}, abstractNote={Summary}, number={1}, journal={BIOMETRICS}, author={Vock, Laura F. Boehm and Reich, Brian J. and Fuentes, Montserrat and Dominici, Francesca}, year={2015}, month={Mar}, pages={167–177} }
 @article{stingone_luben_daniels_fuentes_richardson_aylsworth_herring_anderka_botto_correa_et al._2014, title={Maternal Exposure to Criteria Air Pollutants and Congenital Heart Defects in Offspring: Results from the National Birth Defects Prevention Study}, volume={122}, ISSN={["1552-9924"]}, DOI={10.1289/ehp.1307289}, abstractNote={Background: Epidemiologic literature suggests that exposure to air pollutants is associated with fetal development. Objectives: We investigated maternal exposures to air pollutants during weeks 2–8 of pregnancy and their associations with congenital heart defects. Methods: Mothers from the National Birth Defects Prevention Study, a nine-state case–control study, were assigned 1-week and 7-week averages of daily maximum concentrations of carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide and 24-hr measurements of fine and coarse particulate matter using the closest air monitor within 50 km to their residence during early pregnancy. Depending on the pollutant, a maximum of 4,632 live-birth controls and 3,328 live-birth, fetal-death, or electively terminated cases had exposure data. Hierarchical regression models, adjusted for maternal demographics and tobacco and alcohol use, were constructed. Principal component analysis was used to assess these relationships in a multipollutant context. Results: Positive associations were observed between exposure to nitrogen dioxide and coarctation of the aorta and pulmonary valve stenosis. Exposure to fine particulate matter was positively associated with hypoplastic left heart syndrome but inversely associated with atrial septal defects. Examining individual exposure-weeks suggested associations between pollutants and defects that were not observed using the 7-week average. Associations between left ventricular outflow tract obstructions and nitrogen dioxide and between hypoplastic left heart syndrome and particulate matter were supported by findings from the multipollutant analyses, although estimates were attenuated at the highest exposure levels. Conclusions: Using daily maximum pollutant levels and exploring individual exposure-weeks revealed some positive associations between certain pollutants and defects and suggested potential windows of susceptibility during pregnancy. Citation: Stingone JA, Luben TJ, Daniels JL, Fuentes M, Richardson DB, Aylsworth AS, Herring AH, Anderka M, Botto L, Correa A, Gilboa SM, Langlois PH, Mosley B, Shaw GM, Siffel C, Olshan AF, National Birth Defects Prevention Study. 2014. Maternal exposure to criteria air pollutants and congenital heart defects in offspring: results from the National Birth Defects Prevention Study. Environ Health Perspect 122:863–872; http://dx.doi.org/10.1289/ehp.1307289}, number={8}, journal={ENVIRONMENTAL HEALTH PERSPECTIVES}, author={Stingone, Jeanette A. and Luben, Thomas J. and Daniels, Julie L. and Fuentes, Montserrat and Richardson, David B. and Aylsworth, Arthur S. and Herring, Amy H. and Anderka, Marlene and Botto, Lorenzo and Correa, Adolfo and et al.}, year={2014}, month={Aug}, pages={863–872} }
 @article{mannshardt_sucic_jiao_dominici_frey_reich_fuentes_2013, title={Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions}, volume={23}, ISSN={["1559-064X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84886725530&partnerID=MN8TOARS}, DOI={10.1038/jes.2013.39}, abstractNote={A crucial step in an epidemiological study of the effects of air pollution is to accurately quantify exposure of the population. In this paper, we investigate the sensitivity of the health effects estimates associated with short-term exposure to fine particulate matter with respect to three potential metrics for daily exposure: ambient monitor data, estimated values from a deterministic atmospheric chemistry model, and stochastic daily average human exposure simulation output. Each of these metrics has strengths and weaknesses when estimating the association between daily changes in ambient exposure to fine particulate matter and daily emergency hospital admissions. Monitor data is readily available, but is incomplete over space and time. The atmospheric chemistry model output is spatially and temporally complete but may be less accurate than monitor data. The stochastic human exposure estimates account for human activity patterns and variability in pollutant concentration across microenvironments, but requires extensive input information and computation time. To compare these metrics, we consider a case study of the association between fine particulate matter and emergency hospital admissions for respiratory cases for the Medicare population across three counties in New York. Of particular interest is to quantify the impact and/or benefit to using the stochastic human exposure output to measure ambient exposure to fine particulate matter. Results indicate that the stochastic human exposure simulation output indicates approximately the same increase in the relative risk associated with emergency admissions as using a chemistry model or monitoring data as exposure metrics. However, the stochastic human exposure simulation output and the atmospheric chemistry model both bring additional information, which helps to reduce the uncertainly in our estimated risk.}, number={6}, journal={JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY}, author={Mannshardt, Elizabeth and Sucic, Katarina and Jiao, Wan and Dominici, Francesca and Frey, H. Christopher and Reich, Brian and Fuentes, Montserrat}, year={2013}, pages={627–636} }
 @article{fuentes_reich_2013, title={Multivariate spatial nonparametric modelling via kernel processes mixing}, volume={23}, number={1}, journal={Statistica Sinica}, author={Fuentes, M. and Reich, B.}, year={2013}, pages={75–97} }
 @article{fuentes_henry_reich_2013, title={Nonparametric spatial models for extremes: application to extreme temperature data}, volume={16}, ISSN={["1572-915X"]}, DOI={10.1007/s10687-012-0154-1}, abstractNote={Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events, as opposed to just the mean, to locations where observations are not available. Another related issue is the need to characterize the uncertainty in the estimated probability of extreme events at different locations. Although the tools for statistical modeling of univariate extremes are well-developed, extending these tools to model spatial extreme data is an active area of research. In this paper, in order to make inference about spatial extreme events, we introduce a new nonparametric model for extremes. We present a Dirichlet-based copula model that is a flexible alternative to parametric copula models such as the normal and t-copula. The proposed modelling approach is fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models. We apply our methods to annual maximum temperature values in the east-south-central United States.}, number={1}, journal={EXTREMES}, author={Fuentes, Montserrat and Henry, John and Reich, Brian}, year={2013}, month={Mar}, pages={75–101} }
 @article{warren_fuentes_herring_langlois_2012, title={Bayesian spatial-temporal model for cardiac congenital anomalies and ambient air pollution risk assessment}, volume={23}, ISSN={["1099-095X"]}, DOI={10.1002/env.2174}, abstractNote={We introduce a Bayesian spatial–temporal hierarchical multivariate probit regression model that identifies weeks during the first trimester of pregnancy, which are impactful in terms of cardiac congenital anomaly development. The model is able to consider multiple pollutants and a multivariate cardiac anomaly grouping outcome jointly while allowing the critical windows to vary in a continuous manner across time and space. We utilize a dataset of numerical chemical model output that contains information regarding multiple species of PM 2.5. Our introduction of an innovative spatial–temporal semiparametric prior distribution for the pollution risk effects allows for greater flexibility to identify critical weeks during pregnancy, which are missed when more standard models are applied. The multivariate kernel stick‐breaking prior is extended to include space and time simultaneously in both the locations and the masses in order to accommodate complex data settings. Simulation study results suggest that our prior distribution has the flexibility to outperform competitor models in a number of data settings. When applied to the geo‐coded Texas birth data, weeks 3, 7 and 8 of the pregnancy are identified as being impactful in terms of cardiac defect development for multiple pollutants across the spatial domain. Copyright © 2012 John Wiley & Sons, Ltd.}, number={8}, journal={ENVIRONMETRICS}, author={Warren, Joshua and Fuentes, Montserrat and Herring, Amy and Langlois, Peter}, year={2012}, month={Dec}, pages={673–684} }
 @article{modlin_fuentes_reich_2012, title={Circular conditional autoregressive modeling of vector fields}, volume={23}, ISSN={["1180-4009"]}, DOI={10.1002/env.1133}, abstractNote={As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our circular conditional autoregressive model to prior methods that decompose wind speed and direction into its N‐S and W‐E cardinal components. Copyright © 2011 John Wiley & Sons, Ltd.}, number={1}, journal={ENVIRONMETRICS}, author={Modlin, Danny and Fuentes, Montserrat and Reich, Brian}, year={2012}, month={Feb}, pages={46–53} }
 @article{zhou_chang_fuentes_2012, title={Estimating the Health Impact of Climate Change With Calibrated Climate Model Output}, volume={17}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-012-0105-y}, abstractNote={Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using nonlinear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of of excess deaths attributed to future temperature for three cities in the US state of Alabama.}, number={3}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Zhou, Jingwen and Chang, Howard H. and Fuentes, Montserrat}, year={2012}, month={Sep}, pages={377–394} }
 @article{reich_fuentes_2012, title={Nonparametric Bayesian models for a spatial covariance}, volume={9}, ISSN={["1572-3127"]}, DOI={10.1016/j.stamet.2011.01.007}, abstractNote={A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.}, number={1-2}, journal={STATISTICAL METHODOLOGY}, author={Reich, Brian J. and Fuentes, Montserrat}, year={2012}, pages={265–274} }
 @article{warren_fuentes_herring_langlois_2012, title={Spatial-Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure}, volume={68}, ISSN={["0006-341X"]}, DOI={10.1111/j.1541-0420.2012.01774.x}, abstractNote={Summary Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM2.5, ozone) while allowing these windows to vary continuously across space and time. We geo‐code vital record birth data from Texas (2002–2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows.}, number={4}, journal={BIOMETRICS}, author={Warren, Joshua and Fuentes, Montserrat and Herring, Amy and Langlois, Peter}, year={2012}, month={Dec}, pages={1157–1167} }
 @article{chang_fuentes_frey_2012, title={Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator}, volume={22}, ISSN={["1559-064X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84865498111&partnerID=MN8TOARS}, DOI={10.1038/jes.2012.53}, abstractNote={This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5 μm in aerodynamic diameter (PM2.5) and daily mortality in the New York City metropolitan area during the period 2001–2005. Personal PM2.5 exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 μg/m3 increase in personal exposure to PM2.5 from outdoor sources on the previous day. The corresponding estimates per a 10 μg/m3 increase in PM2.5 ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM2.5 were also higher during the summer months.}, number={5}, journal={JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY}, author={Chang, Howard H. and Fuentes, Montserrat and Frey, H. Christopher}, year={2012}, pages={483–488} }
 @article{reich_kalendra_storlie_bondell_fuentes_2012, title={Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation}, volume={61}, journal={Journal of the Royal Statistical Society. Series C, Applied Statistics}, author={Reich, B. J. and Kalendra, E. and Storlie, C. B. and Bondell, H. D. and Fuentes, M.}, year={2012}, pages={47–66} }
 @article{reich_fuentes_dunson_2011, title={Bayesian Spatial Quantile Regression}, volume={106}, ISSN={["1537-274X"]}, DOI={10.1198/jasa.2010.ap09237}, abstractNote={Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We develop a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. For extremely large datasets our model is computationally infeasible, and we develop an approximate method. We apply the approximate version of our model to summer ozone from 1997-2005 in the Eastern U.S., and use deterministic climate models to project ozone under future climate conditions. Our analysis suggests that holding all other factors fixed, an increase in daily average temperature will lead to the largest increase in ozone in the Industrial Midwest and Northeast.}, number={493}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Reich, Brian J. and Fuentes, Montserrat and Dunson, David B.}, year={2011}, month={Mar}, pages={6–20} }
 @article{zhou_fuentes_davis_2011, title={Calibration of Numerical Model Output Using Nonparametric Spatial Density Functions}, volume={16}, ISSN={["1085-7117"]}, DOI={10.1007/s13253-011-0076-4}, abstractNote={The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of the means and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for the assessment of model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution. Improving the ability of these numerical models to characterize high pollution events is of critical interest for air quality management. In this work we introduce an innovative framework to assess model performance, not only based on the two first moments of the model outputs and field data, but also on their entire distributions. Our approach also compares the spatial dependence and variability in two data sources. More specifically, we estimate the spatial-quantile functions for both the model output and field data, and we apply a nonlinear monotonic regression approach to the quantile functions taking into account the spatial dependence to compare the density functions of numerical models and field data. We use a Bayesian approach for estimation and fitting to characterize uncertainties in data and statistical models. We apply our methodology to assess the performance of the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model to characterize ozone ambient concentrations. Our approach shows a 50.23% reduction in the root mean square error (RMSE) compared to the default approach based on the first 2 moments of the model output and field data.}, number={4}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Zhou, Jingwen and Fuentes, Montserrat and Davis, Jerry}, year={2011}, month={Dec}, pages={531–553} }
 @article{dennis_fox_fuentes_gilliland_hanna_hogrefe_irwin_rao_scheffe_schere_et al._2010, title={A framework for evaluating regional-scale numerical photochemical modeling systems}, volume={10}, ISSN={["1573-1510"]}, DOI={10.1007/s10652-009-9163-2}, abstractNote={This paper discusses the need for critically evaluating regional-scale (~200-2000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.}, number={4}, journal={ENVIRONMENTAL FLUID MECHANICS}, author={Dennis, Robin and Fox, Tyler and Fuentes, Montse and Gilliland, Alice and Hanna, Steven and Hogrefe, Christian and Irwin, John and Rao, S. Trivikrama and Scheffe, Richard and Schere, Kenneth and et al.}, year={2010}, month={Aug}, pages={471–489} }
 @article{reich_fuentes_herring_evenson_2010, title={Bayesian Variable Selection for Multivariate Spatially Varying Coefficient Regression}, volume={66}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2009.01333.x}, abstractNote={Summary Physical activity has many well‐documented health benefits for cardiovascular fitness and weight control. For pregnant women, the American College of Obstetricians and Gynecologists currently recommends 30 minutes of moderate exercise on most, if not all, days; however, very few pregnant women achieve this level of activity. Traditionally, studies have focused on examining individual or interpersonal factors to identify predictors of physical activity. There is a renewed interest in whether characteristics of the physical environment in which we live and work may also influence physical activity levels. We consider one of the first studies of pregnant women that examines the impact of characteristics of the built environment on physical activity levels. Using a socioecologic framework, we study the associations between physical activity and several factors including personal characteristics, meteorological/air quality variables, and neighborhood characteristics for pregnant women in four counties of North Carolina. We simultaneously analyze six types of physical activity and investigate cross‐dependencies between these activity types. Exploratory analysis suggests that the associations are different in different regions. Therefore, we use a multivariate regression model with spatially varying regression coefficients. This model includes a regression parameter for each covariate at each spatial location. For our data with many predictors, some form of dimension reduction is clearly needed. We introduce a Bayesian variable selection procedure to identify subsets of important variables. Our stochastic search algorithm determines the probabilities that each covariate's effect is null, non‐null but constant across space, and spatially varying. We found that individual‐level covariates had a greater influence on women's activity levels than neighborhood environmental characteristics, and some individual‐level covariates had spatially varying associations with the activity levels of pregnant women.}, number={3}, journal={BIOMETRICS}, author={Reich, Brian J. and Fuentes, Montserrat and Herring, Amy H. and Evenson, Kelly R.}, year={2010}, month={Sep}, pages={772–782} }
 @article{chang_zhou_fuentes_2010, title={Impact of climate change on ambient ozone level and mortality in Southeastern United States}, volume={7}, number={7}, journal={International Journal of Environmental Research and Public Health}, author={Chang, H. H. and Zhou, J. W. and Fuentes, M.}, year={2010}, pages={2866–2880} }
 @article{reich_fuentes_burke_2009, title={Analysis of the effects of ultrafine particulate matter while accounting for human exposure}, volume={20}, ISSN={["1099-095X"]}, DOI={10.1002/env.915}, abstractNote={Abstract}, number={2}, journal={ENVIRONMETRICS}, author={Reich, Brian J. and Fuentes, Montserrat and Burke, Janet}, year={2009}, month={Mar}, pages={131–146} }
 @article{choi_fuentes_reich_2009, title={Spatial-temporal association between fine particulate matter and daily mortality}, volume={53}, ISSN={["1872-7352"]}, DOI={10.1016/j.csda.2008.05.018}, abstractNote={Fine particulate matter (PM(2.5)) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM(2.5) varies across space and time, the association between PM(2.5) and mortality could also change with space and season. In this work we develop and implement a statistical multi-stage Bayesian framework that provides a very broad, flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM(2.5) mass, while accounting for different sources of uncertainty. In stage 1, we map ambient PM(2.5) air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. In stage 2, we examine the spatial temporal relationships between the health end-points and the exposures to PM(2.5) by introducing a spatial-temporal generalized Poisson regression model. We adjust for time-varying confounders, such as seasonal trends. A common seasonal trends model is to use a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. In this study, the number of the basis functions is treated as an unknown parameter in our Bayesian model and we use a space-time stochastic search variable selection approach. We apply our methods to a data set in North Carolina for the year 2001.}, number={8}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Choi, Jungsoon and Fuentes, Montserrat and Reich, Brian J.}, year={2009}, month={Jun}, pages={2989–3000} }
 @article{fuentes_chen_davis_2008, title={A class of nonseparable and nonstationary spatial temporal covariance functions}, volume={19}, ISSN={["1099-095X"]}, DOI={10.1002/env.891}, abstractNote={Abstract}, number={5}, journal={ENVIRONMETRICS}, author={Fuentes, Montserrat and Chen, Li and Davis, Jerry M.}, year={2008}, month={Aug}, pages={487–507} }
 @article{song_fuentes_ghosh_2008, title={A comparative study of Gaussian geostatistical models and Gaussian Markov random field models}, volume={99}, ISSN={["0047-259X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-48549087068&partnerID=MN8TOARS}, DOI={10.1016/j.jmva.2008.01.012}, abstractNote={Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two distinct approaches commonly used in spatial models for modeling point-referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed : (i) the Kullback–Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM2.5 collected in California is presented to illustrate the proposed method.}, number={8}, journal={JOURNAL OF MULTIVARIATE ANALYSIS}, author={Song, Hae-Ryoung and Fuentes, Montserrat and Ghosh, Sujit}, year={2008}, month={Sep}, pages={1681–1697} }
 @article{foley_fuentes_2008, title={A statistical framework to combine multivariate spatial data and physical models for hurricane surface wind prediction}, volume={13}, ISSN={["1537-2693"]}, DOI={10.1198/108571108X276473}, abstractNote={Storm surge is the onshore rush of seawater associated with hurricane force winds. Storm surge can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for predicting which coastal areas are most likely to be impacted by storm surge. These numerical physics-based models are driven primarily by the surface wind forcings which are currently specified by a deterministic formula. Although these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. This article develops a new multivariate spatial statistical framework to improve the estimation of these wind field inputs while accounting for potential bias in the observations. We find that this spatial model consistently improves parameter estimation and prediction for surface wind data for a case study of Hurricane Charley of 2004 when compared to the original physical model. These methods are also shown to improve storm surge estimates when used as the forcing fields for a numerical three-dimensional coastal ocean model.}, number={1}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Foley, K. M. and Fuentes, M.}, year={2008}, month={Mar}, pages={37–59} }
 @article{fuentes_2008, title={Comments on: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds}, volume={17}, ISSN={["1863-8260"]}, DOI={10.1007/s11749-008-0119-5}, number={2}, journal={TEST}, author={Fuentes, Montserrat}, year={2008}, month={Aug}, pages={245–248} }
 @article{fuentes_reich_lee_2008, title={SPATIAL-TEMPORAL MESOSCALE MODELING OF RAINFALL INTENSITY USING GAGE AND RADAR DATA}, volume={2}, ISSN={["1932-6157"]}, DOI={10.1214/08-AOAS166}, abstractNote={Gridded estimated rainfall intensity values at very high spatial and temporal resolution levels are needed as main inputs for weather prediction models to obtain accurate precipitation forecasts, and to verify the performance of precipitation forecast models. These gridded rainfall fields are also the main driver for hydrological models that forecast flash floods, and they are essential for disaster prediction associated with heavy rain. Rainfall information can be obtained from rain gages that provide relatively accurate estimates of the actual rainfall values at point-referenced locations, but they do not characterize well enough the spatial and temporal structure of the rainfall fields. Doppler radar data offer better spatial and temporal coverage, but Doppler radar measures effective radar reflectivity (Ze) rather than rainfall rate (R). Thus, rainfall estimates from radar data suffer from various uncertainties due to their measuring principle and the conversion from Ze to R. We introduce a framework to combine radar reflectivity and gage data, by writing the different sources of rainfall information in terms of an underlying unobservable spatial temporal process with the true rainfall values. We use spatial logistic regression to model the probability of rain for both sources of data in terms of the latent true rainfall process. We characterize the different sources of bias and error in the gage and radar data and we estimate the true rainfall intensity with its posterior predictive distribution, conditioning on the observed data. Our model allows for nonstationary and asymmetry in the spatio-temporal dependency structure of the rainfall process, and allows the temporal evolution of the rainfall process to depend on the motions of rain fields, and the spatial correlation to depend on geographic features. We apply our methods to estimate rainfall intensity every 10 minutes, in a subdomain over South Korea with a spatial resolution of 1km by 1km.}, number={4}, journal={ANNALS OF APPLIED STATISTICS}, author={Fuentes, Montserrat and Reich, Brian and Lee, Gyuwon}, year={2008}, month={Dec}, pages={1148–1169} }
 @article{fuentes_guttorp_stein_2008, title={SPECIAL SECTION ON STATISTICS IN THE ATMOSPHERIC SCIENCES}, volume={2}, ISSN={["1932-6157"]}, DOI={10.1214/08-AOAS209}, abstractNote={With the possible exception of gambling, meteorology, particularly precipitation forecasting, may be the area with which the general public is most familiar with probabilistic assessments of uncertainty. Despite the heavy use of stochastic models and statistical methods in weather forecasting and other areas of the atmospheric sciences, papers in these areas have traditionally been somewhat uncommon in statistics journals. We see signs of this changing in recent years and we have sought to highlight some present research directions at the interface of statistics and the atmospheric sciences in this special section.}, number={4}, journal={ANNALS OF APPLIED STATISTICS}, author={Fuentes, Montserrat and Guttorp, Peter and Stein, Michael L.}, year={2008}, month={Dec}, pages={1143–1147} }
 @article{park_fuentes_2008, title={Testing lack of symmetry in spatial-temporal processes}, volume={138}, ISSN={["1873-1171"]}, DOI={10.1016/j.jspi.2007.10.021}, abstractNote={Symmetry and separability of spatial-temporal covariances are the main assumptions that are frequently taken for granted in most applications because of the simplicity of constructing covariance structure. However, many studies in environmental sciences show that real data have complex spatial-temporal dependency structures resulting from lack of symmetry or violation of other standard assumptions of the covariance function. In this study, we propose new formal tests for lack of symmetry by using spectral representations of the spatial-temporal covariance functions. The advantage of the proposed tests is that classical analysis of variance (ANOVA) models can be used for detecting lack of symmetry inherent in spatial-temporal processes. We evaluate the performance of the tests with simulation studies and we apply them to air pollution data.}, number={10}, journal={JOURNAL OF STATISTICAL PLANNING AND INFERENCE}, author={Park, Man Sik and Fuentes, Montserrat}, year={2008}, month={Oct}, pages={2847–2866} }
 @article{reich_fuentes_2007, title={A MULTIVARIATE SEMIPARAMETRIC BAYESIAN SPATIAL MODELING FRAMEWORK FOR HURRICANE SURFACE WIND FIELDS}, volume={1}, ISSN={["1932-6157"]}, DOI={10.1214/07-AOAS108}, abstractNote={Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan.}, number={1}, journal={ANNALS OF APPLIED STATISTICS}, author={Reich, Brian J. and Fuentes, Montserrat}, year={2007}, month={Jun}, pages={249–264} }
 @article{fuentes_2007, title={Approximate likelihood for large irregularly spaced spatial data}, volume={102}, ISSN={["0162-1459"]}, DOI={10.1198/016214506000000852}, abstractNote={Likelihood approaches for large, irregularly spaced spatial datasets are often very difficult, if not infeasible, to implement due to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at n locations requires O(n3) operations. We present a version of Whittle's approximation to the Gaussian log-likelihood for spatial regular lattices with missing values and for irregularly spaced datasets. This method requires O(nlog2n) operations and does not involve calculating determinants. We present simulations and theoretical results to show the benefits and the performance of the spatial likelihood approximation method presented here for spatial irregularly spaced datasets and lattices with missing values. We apply these methods to estimate the spatial structure of sea surface temperatures using satellite data with missing values.}, number={477}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Fuentes, Montserrat}, year={2007}, month={Mar}, pages={321–331} }
 @article{fuentes_chaudhuri_holland_2007, title={Bayesian entropy for spatial sampling design of environmental data}, volume={14}, ISSN={["1573-3009"]}, DOI={10.1007/s10651-007-0017-0}, abstractNote={We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.}, number={3}, journal={ENVIRONMENTAL AND ECOLOGICAL STATISTICS}, author={Fuentes, Montserrat and Chaudhuri, Arin and Holland, David M.}, year={2007}, month={Sep}, pages={323–340} }
 @article{xie_bao_pietrafesa_foley_fuentes_2006, title={A real-time hurricane surface wind forecasting model: Formulation and verification}, volume={134}, ISSN={["0027-0644"]}, DOI={10.1175/MWR3126.1}, abstractNote={Abstract}, number={5}, journal={MONTHLY WEATHER REVIEW}, author={Xie, L and Bao, SW and Pietrafesa, LJ and Foley, K and Fuentes, M}, year={2006}, month={May}, pages={1355–1370} }
 @article{fuentes_kittel_nychka_2006, title={Sensitivity of ecological models to their climate drivers: Statistical ensembles for forcing}, volume={16}, ISSN={["1939-5582"]}, DOI={10.1890/04-1157}, abstractNote={Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.}, number={1}, journal={ECOLOGICAL APPLICATIONS}, author={Fuentes, M and Kittel, TGF and Nychka, D}, year={2006}, month={Feb}, pages={99–116} }
 @article{fuentes_song_ghosh_holland_davis_2006, title={Spatial association between speciated fine particles and mortality}, volume={62}, ISSN={["1541-0420"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33748768427&partnerID=MN8TOARS}, DOI={10.1111/j.1541-0420.2006.00526.x}, abstractNote={Summary Particulate matter (PM) has been linked to a range of serious cardiovascular and respiratory health problems, including premature mortality. The main objective of our research is to quantify uncertainties about the impacts of fine PM exposure on mortality. We develop a multivariate spatial regression model for the estimation of the risk of mortality associated with fine PM and its components across all counties in the conterminous United States. We characterize different sources of uncertainty in the data and model the spatial structure of the mortality data and the speciated fine PM. We consider a flexible Bayesian hierarchical model for a space‐time series of counts (mortality) by constructing a likelihood‐based version of a generalized Poisson regression model that combines methods for point‐level misaligned data and change of support regression. Our results seem to suggest an increase by a factor of two in the risk of mortality due to fine particles with respect to coarse particles. Our study also shows that in the Western United States, the nitrate and crustal components of the speciated fine PM seem to have more impact on mortality than the other components. On the other hand, in the Eastern United States, sulfate and ammonium explain most of the fine PM effect.}, number={3}, journal={BIOMETRICS}, author={Fuentes, Montserrat and Song, Hae-Ryoung and Ghosh, Sujit K. and Holland, David M. and Davis, Jerry M.}, year={2006}, month={Sep}, pages={855–863} }
 @article{fuentes_2006, title={Testing for separability of spatial-temporal covariance functions}, volume={136}, ISSN={["1873-1171"]}, DOI={10.1016/j.jspi.2004.07.004}, abstractNote={Most applications in spatial statistics involve modeling of complex spatial–temporal dependency structures, and many of the problems of space and time modeling can be overcome by using separable processes. This subclass of spatial–temporal processes has several advantages, including rapid fitting and simple extensions of many techniques developed and successfully used in time series and classical geostatistics. In particular, a major advantage of these processes is that the covariance matrix for a realization can be expressed as the Kronecker product of two smaller matrices that arise separately from the temporal and purely spatial processes, and hence its determinant and inverse are easily determinable. However, these separable models are not always realistic, and there are no formal tests for separability of general spatial–temporal processes. We present here a formal method to test for separability. Our approach can be also used to test for lack of stationarity of the process. The beauty of our approach is that by using spectral methods the mechanics of the test can be reduced to a simple two-factor analysis of variance (ANOVA) procedure. The approach we propose is based on only one realization of the spatial–temporal process. We apply the statistical methods proposed here to test for separability and stationarity of spatial–temporal ozone fields using data provided by the US Environmental Protection Agency (EPA).}, number={2}, journal={JOURNAL OF STATISTICAL PLANNING AND INFERENCE}, author={Fuentes, M}, year={2006}, month={Feb}, pages={447–466} }
 @article{flores_allen_cheshire_davis_fuentes_kelting_2006, title={Using multispectral satellite imagery to estimate leaf area and response to silvicultural treatments in loblolly pine stands}, volume={36}, ISSN={["0045-5067"]}, DOI={10.1139/X06-030}, abstractNote={ The relationship between leaf area index (LAI) of loblolly pine plantations and the broadband simple ratio (SR) vegetation index calculated from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data was examined. An equation was derived to estimate LAI from readily available Landsat 7 ETM+ data. The equation developed to predict LAI with Landsat 7 ETM+ data was tested with ground LAI measurements taken in 12 plots. The root mean square error of prediction was 0.29, an error of approximately 14% in prediction. The ability of Landsat 7 ETM+ data to consistently estimate SR over time was tested using two scenes acquired on different dates during the winter (December to early March). Comparison between the two images on a pixel-by-pixel basis showed that approximately 96% of the pixels had a difference of <0.5 units of SR (approximately 0.3 units of LAI). When the comparison was made on a stand-by-stand basis (average stand SR), a maximum difference of 0.2 units of SR (approximately 0.12 units of LAI) was found. These results suggest that stand LAI of loblolly pine plantations can be accurately estimated from readily available remote sensing data and provide an opportunity to apply the findings from ecophysiological studies in field plots to forest management decisions at an operational scale. }, number={6}, journal={CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE}, author={Flores, FJ and Allen, HL and Cheshire, HM and Davis, JM and Fuentes, M and Kelting, D}, year={2006}, month={Jun}, pages={1587–1596} }
 @article{fuentes_2005, title={A formal test for nonstationarity of spatial stochastic processes}, volume={96}, ISSN={["0047-259X"]}, DOI={10.1016/j.jmva.2004.09.003}, abstractNote={Spatial statistics is one of the major methodologies of image analysis, field trials, remote sensing, and environmental statistics. The standard methodology in spatial statistics is essentially based on the assumption of stationary and isotropic random fields. Such assumptions might not hold in large heterogeneous fields. Thus, it is important to understand when stationarity and isotropy are reasonable assumptions. Most of the work that has been done so far to test the nonstationarity of a random process is in one dimension. Unfortunately, there is not much literature of formal procedures to test for stationarity of spatial stochastic processes. In this manuscript, we consider the problem of testing a given spatial process for stationarity and isotropy. The approach is based on a spatial spectral analysis, this means spectral functions which are space dependent. The proposed method consists essentially in testing the homogeneity of a set of spatial spectra evaluated at different locations. In addition to testing stationarity and isotropy, the analysis provides also a method for testing whether the observed process fits a uniformly modulated model, and a test for randomness (white noise). Applications include modeling and testing for nonstationary of air pollution concentrations over different geo-political boundaries.}, number={1}, journal={JOURNAL OF MULTIVARIATE ANALYSIS}, author={Fuentes, M}, year={2005}, month={Sep}, pages={30–54} }
 @article{fuentes_raftery_2005, title={Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models}, volume={61}, ISSN={["1541-0420"]}, DOI={10.1111/j.0006-341X.2005.030821.x}, abstractNote={Summary Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models‐3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models‐3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models‐3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models‐3 output. We apply our methods to data on SO2 concentrations, and we obtain high‐resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.}, number={1}, journal={BIOMETRICS}, author={Fuentes, M and Raftery, AE}, year={2005}, month={Mar}, pages={36–45} }
 @article{fuentes_chen_davis_lackmann_2005, title={Modeling and predicting complex space-time structures and patterns of coastal wind fields}, volume={16}, ISSN={["1099-095X"]}, DOI={10.1002/env.714}, abstractNote={Abstract}, number={5}, journal={ENVIRONMETRICS}, author={Fuentes, M and Chen, L and Davis, JM and Lackmann, GM}, year={2005}, month={Aug}, pages={449–464} }
 @article{fuentes_boos_2005, title={Special issue - Environmental and health statistics}, volume={16}, ISSN={["1180-4009"]}, DOI={10.1002/env.711}, abstractNote={EnvironmetricsVolume 16, Issue 5 p. 421-421 Editorial Editorial Montserrat Fuentes, Montserrat Fuentes Profs. North Carolina State University, Raleigh, N.C., U.S.A.Search for more papers by this authorDennis D. Boos, Dennis D. Boos North Carolina State University, Raleigh, N.C., U.S.A.Search for more papers by this author Montserrat Fuentes, Montserrat Fuentes Profs. North Carolina State University, Raleigh, N.C., U.S.A.Search for more papers by this authorDennis D. Boos, Dennis D. Boos North Carolina State University, Raleigh, N.C., U.S.A.Search for more papers by this author First published: 27 June 2005 https://doi.org/10.1002/env.711AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume16, Issue5Special Issue: Environmental and Health StatisticsAugust 2005Pages 421-421 RelatedInformation}, number={5}, journal={ENVIRONMETRICS}, author={Fuentes, M and Boos, DD}, year={2005}, month={Aug}, pages={421–421} }
 @article{fuentes_higdon_sanso_gelfand_schmidt_banerjee_sirmans_2004, title={Nonstationary multivariate process modeling through spatially varying coregionalization - Discussion}, volume={13}, number={2}, journal={TEST}, author={Fuentes, M. and Higdon, D. and Sanso, B. and Gelfand, A. E. and Schmidt, A. M. and Banerjee, S. and Sirmans, C. F.}, year={2004}, pages={295–312} }
 @article{yuen_erlebacher_vasilyev_goldstein_fuentes_2004, title={Role of wavelets in the physical and statistical modelling of complex geological processes}, volume={161}, ISSN={["1420-9136"]}, DOI={10.1007/s00024-004-2560-z}, number={11-12}, journal={PURE AND APPLIED GEOPHYSICS}, author={Yuen, DA and Erlebacher, G and Vasilyev, OV and Goldstein, DE and Fuentes, M}, year={2004}, month={Dec}, pages={2231–2244} }
 @article{doney_glover_mccue_fuentes_2003, title={Mesoscale variability of Sea-viewing Wide Field-of-view Sensor(SeaWiFS) satellite ocean color: Global patterns and spatial scales}, volume={108}, number={C2}, journal={Journal of Geophysical Research. Oceans}, author={Doney, S. C. and Glover, D. M. and Mccue, S. J. and Fuentes, M.}, year={2003}, pages={3024–1} }
 @article{mateu_montes_fuentes_2003, title={Recent advances in space-time statistics with applications to environmental data: An overview}, volume={108}, ISSN={["2169-8996"]}, DOI={10.1029/2003jd003819}, abstractNote={This paper introduces a special section based on general environmental scientific problems, with a particular focus on using atmospheric data. All the papers and authors provide the methodology to study, analyze, predict, and evaluate the spatial‐temporal behavior and the complicated spatial‐temporal structure of the data. The overall aim is to present up‐to‐date developments in spatial and spatiotemporal statistics in the field of the atmosphere, to present on‐going research, and to discuss important problems to be addressed in the near future.}, number={D24}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, author={Mateu, J and Montes, F and Fuentes, M}, year={2003}, month={Nov} }
 @article{fuentes_2003, title={Statistical assessment of geographic areas of compliance with air quality standards}, volume={108}, number={D24}, journal={Journal of Geophysical Research. Atmospheres}, author={Fuentes, M.}, year={2003} }
 @article{fuentes_guttorp_challenor_2003, title={Statistical assessment of numerical models}, volume={71}, number={2}, journal={International Statistical Review}, author={Fuentes, M. and Guttorp, P. and Challenor, P.}, year={2003}, pages={201–221} }
 @article{fuentes_2002, title={Spectral methods for nonstationary spatial processes}, volume={89}, ISSN={["0006-3444"]}, DOI={10.1093/biomet/89.1.197}, abstractNote={SUMMARY We propose a nonstationary periodogram and various parametric approaches for estimating the spectral density of a nonstationary spatial process. We also study the asymptotic properties of the proposed estimators via shrinking asymptotics, assuming the distance between neighbouring observations tends to zero as the size of the observation region grows without bound. With this type of asymptotic model we can uniquely determine the spectral density, avoiding the aliasing problem. We also present a new class of nonstationary processes, based on a convolution of local stationary processes. This model has the advantage that the model is simultaneously defined everywhere, unlike 'moving window' approaches, but it retains the attractive property that, locally in small regions, it behaves like a stationary spatial process. Applications include the spatial analysis and modelling of air pollution data provided by the US Environmental Protection Agency.}, number={1}, journal={BIOMETRIKA}, author={Fuentes, M}, year={2002}, month={Mar}, pages={197–210} }
 @article{fuentes_2001, title={A high frequency kriging approach for non-stationary environmental processes}, volume={12}, DOI={10.1002/env.473}, abstractNote={Abstract}, number={5}, journal={Environmetrics}, author={Fuentes, M.}, year={2001}, pages={469–483} }
 @article{fuentes_2001, title={Fixed-domain asymptotics for variograms using subsampling}, volume={33}, ISSN={["0882-8121"]}, DOI={10.1023/A:1011074615343}, number={6}, journal={MATHEMATICAL GEOLOGY}, author={Fuentes, M}, year={2001}, month={Aug}, pages={679–691} }
 @article{fuentes_2000, title={Predicting integrals of diffusion processes}, volume={90}, ISSN={["0378-3758"]}, DOI={10.1016/S0378-3758(00)00121-X}, abstractNote={Consider predicting the integral of a diffusion process Z in a bounded interval A, based on the observations Z(t1n),…,Z(tnn), where t1n,…,tnn is a dense triangular array of points (the step of discretization tends to zero as n increases) in the bounded interval. The best linear predictor is generally not asymptotically optimal. Instead, we predict ∫AZ(t)dt using the conditional expectation of the integral of the diffusion process, the optimal predictor in terms of minimizing the mean squared error, given the observed values of the process. We obtain that, conditioning on the observed values, the order of convergence in probability to zero of the mean squared prediction error is Op(n−2). We prove that the standardized conditional prediction error is approximately Gaussian with mean zero and unit variance, even though the underlying diffusion is generally non-Gaussian. Because the optimal predictor is hard to calculate exactly for most diffusions, we present an easily computed approximation that is asymptotically optimal. This approximation is a function of the diffusion coefficient.}, number={2}, journal={JOURNAL OF STATISTICAL PLANNING AND INFERENCE}, author={Fuentes, M}, year={2000}, month={Oct}, pages={183–193} }
 @article{smith_spitzner_kim_fuentes_2000, title={Threshold dependence of mortality effects for fine and coarse particles in Phoenix, Arizona}, volume={50}, ISSN={["1047-3289"]}, DOI={10.1080/10473289.2000.10464172}, abstractNote={ABSTRACT Daily data for fine (<2.5 um) and coarse (2.5-10 um) particles are available for 1995-1997 from the U.S. Environmental Protection Agency (EPA) research monitor in Phoenix, AZ. Mortality effects on the 65 and over population were studied for both the city of Phoenix and for a region of about 50 mi around Phoenix. Coarse particles in Phoenix are believed to be natural in origin and spatially homogeneous, whereas fine particles are primarily vehicular in origin and concentrated in the city itself. For this reason, it is natural to focus on city mortality data when considering fine particles, and on region mortality data when considering coarse particles, and most of the results reported here correspond to those assignments. After allowing for seasonality and long-term trend through a nonlinear (B-spline) trend curve, and also for meteorological effects based on temperature and specific humidity, a regression of mortality was performed on PM using several different measures for PM. Based on a linear PM effect, we found a statistically significant coefficient for coarse particles, but not for fine particles, contrary to what is widely believed about the effects of coarse and fine particles. An analysis of nonlinear pollution-mortality relationships, however, suggests that the true picture is more complicated than that. For coarse particles, the evidence for any nonlinear or threshold-based effect is slight. For fine particles, we found evidence of a threshold, most likely with values in the range of 20-25 ug/m3. We also found some evidence of interactions of the PM effects with season and year. The main effect here is an apparent seasonal interaction in the coarse PM effect. An attempt was made to explain this in terms of seasonal variation in the chemical composition of PM, but this led to another counterintuitive result: the PM effect is highest in spring and summer, when the anthropogenic concentration of coarse PM is lowest as determined by a principal components analysis. There was no evidence of confounding between the fine and coarse PM effects. Although these results are based on one city and should be considered tentative until replicated in other studies, they suggest that the prevailing focus on fine rather than coarse particles may be an oversimplification. The study also shows that consideration of nonlinear effects can lead to real changes of interpretation and raises the possibility of seasonal effects associated with the chemical composition of PM.}, number={8}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Smith, RL and Spitzner, D and Kim, Y and Fuentes, M}, year={2000}, month={Aug}, pages={1367–1379} }