Brian Reich

Miller, M. J. J., & Reich, B. J. J. (2022). Bayesian spatial modeling using random Fourier frequencies. SPATIAL STATISTICS, 4. https://doi.org/10.1016/j.spasta.2022.100598

Reich, B. J., Yang, S., & Guan, Y. (2022, March 30). Discussion on "Spatial plus : A novel approach to spatial confounding" by Dupont, Wood, and Augustin. BIOMETRICS, Vol. 3. https://doi.org/10.1111/biom.13651

Vargas, A. C., Castaneda, J. P., Liljedahl, E. R., Mora, A. M., Menezes-Filho, J. A., Smith, D. R., … Joode, B. van W. (2022). Exposure to common-use pesticides, manganese, lead, and thyroid function among pregnant women from the Infants' Environmental Health (ISA) study, Costa Rica. SCIENCE OF THE TOTAL ENVIRONMENT, 3. https://doi.org/10.1016/j.scitotenv.2021.151288

Islam, J. Y., Hoppin, J., Mora, A. M., Soto-Martinez, M. E., Cordoba Gamboa, L., Penaloza Castaneda, J. E., … Joode, B. (2022, February 24). Respiratory and allergic outcomes among 5-year-old children exposed to pesticides. THORAX, Vol. 2. https://doi.org/10.1136/thoraxjnl-2021-218068

Parsons, A. W., Dawrs, S. N., Nelson, S. T., Norton, G. J., Virdi, R., Hasan, N. A., … Honda, J. R. (2022, April 18). Soil Properties and Moisture Synergistically Influence Nontuberculous Mycobacterial Prevalence in Natural Environments of Hawai'i. APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Vol. 4. https://doi.org/10.1128/aem.00018-22

Euan, C., Sun, Y., & Reich, B. J. (2022, January 20). Statistical analysis of multi-day solar irradiance using a threshold time series model. ENVIRONMETRICS, Vol. 1. https://doi.org/10.1002/env.2716

Tian, Y., & Reich, B. J. (2021). A BAYESIAN SEMI-PARAMETRIC MIXTURE MODEL FOR BIVARIATE EXTREME VALUE ANALYSIS WITH APPLICATION TO PRECIPITATION FORECASTING. STATISTICA SINICA, 7. https://doi.org/10.5705/ss.202018.0420

Reich, B. J., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J., & Rappold, A. (2021, May 31). A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications. INTERNATIONAL STATISTICAL REVIEW, Vol. 5. https://doi.org/10.1111/insr.12452

Lan, Z., Reich, B. J., & Bandyopadhyay, D. (2021). A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE. https://doi.org/10.1002/cjs.11601

Guan, Q., Reich, B. J., & Laber, E. B. (2021, April 10). A spatiotemporal recommendation engine for malaria control. BIOSTATISTICS, Vol. 4. https://doi.org/10.1093/biostatistics/kxab010

Miller, M. J., Cabral, M. J., Dickey, E. C., LeBeau, J. M., & Reich, B. J. (2021, April 28). Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials. TECHNOMETRICS, Vol. 4. https://doi.org/10.1080/00401706.2021.1905070

Xu, S. G., & Reich, B. J. (2021, November 10). Bayesian nonparametric quantile process regression and estimation of marginal quantile effects. BIOMETRICS, Vol. 11. https://doi.org/10.1111/biom.13576

Cui, C., Singh, S. P., Staicu, A.-M., & Reich, B. J. (2021, May 24). Bayesian variable selection for high-dimensional rank data. ENVIRONMETRICS, Vol. 5. https://doi.org/10.1002/env.2682

Li, R., Reich, B. J., & Bondell, H. D. (2021). Deep distribution regression. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 159. https://doi.org/10.1016/j.csda.2021.107203

Alhanti, B., Joode, B. van W., Martinez, M. S., Mora, A. M., Gamboa, L. C., Reich, B., … Hoppin, J. A. (2021, December 30). Environmental exposures contribute to respiratory and allergic symptoms among women living in the banana growing regions of Costa Rica. OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, Vol. 12. https://doi.org/10.1136/oemed-2021-107611

Sass, D., Li, B., & Reich, B. J. (2021, July 18). Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 7. https://doi.org/10.1080/10618600.2021.1938584

Lan, Z., Reich, B. J., Guinness, J., Bandyopadhyay, D., Ma, L., & Moeller, F. G. (2021). Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging. BIOMETRICS. https://doi.org/10.1111/biom.13445

Dorman, S. J., Hopperstad, K. A., Reich, B. J., Majumder, S., Kennedy, G., Reisig, D. D., … Huseth, A. S. (2021, August 18). Landscape-level variation in Bt crops predict Helicoverpa zea (Lepidoptera: Noctuidae) resistance in cotton agroecosystems. PEST MANAGEMENT SCIENCE, Vol. 8. https://doi.org/10.1002/ps.6585

Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. REMOTE SENSING OF ENVIRONMENT, 8. https://doi.org/10.1016/j.rse.2021.112484

Wendelberger, L. J., Reich, B. J., & Wilson, A. G. (2021). Multi-model penalized regression. STATISTICAL ANALYSIS AND DATA MINING. https://doi.org/10.1002/sam.11496

Johnson, M. C., Reich, B. J., & Gray, J. M. (2021, May 21). Multisensor fusion of remotely sensed vegetation indices using space-time dynamic linear models. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol. 5. https://doi.org/10.1111/rssc.12495

Gong, W., Reich, B. J., & Chang, H. H. (2021). Multivariate spatial prediction of air pollutant concentrations with INLA. ENVIRONMENTAL RESEARCH COMMUNICATIONS, 10. https://doi.org/10.1088/2515-7620/ac2f92

Huberman, D. B., Reich, B. J., & Bondell, H. D. (2021, May 20). Nonparametric conditional density estimation in a deep learning framework for short-term forecasting. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, Vol. 5. https://doi.org/10.1007/s10651-021-00499-z

Dorman, S. J., Hopperstad, K. A., Reich, B. J., Kennedy, G., & Huseth, A. S. (2021). Soybeans as a non-Bt refuge for Helicoverpa zea in maize-cotton agroecosystems. AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 12. https://doi.org/10.1016/j.agee.2021.107642

Roy, A., Reich, B. J., Guinness, J., Shinohara, R. T., & Staicu, A.-M. (2021, June 16). Spatial Shrinkage Via the Product Independent Gaussian Process Prior. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 6. https://doi.org/10.1080/10618600.2021.1923512

Larsen, A., Hanigan, I., Reich, B. J., Qin, Y., Cope, M., Morgan, G., & Rappold, A. G. (2020). A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication. JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY. https://doi.org/10.1038/s41370-020-0246-y

Zhang, Y. D., Naughton, B. P., Bondell, H. D., & Reich, B. J. (2020). Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. https://doi.org/10.1080/01621459.2020.1825449

Huberman, D. B., Reich, B. J., Pacifici, K., & Collazo, J. A. (2020). Estimating the drivers of species distributions with opportunistic data using mediation analysis. ECOSPHERE, 11(6). https://doi.org/10.1002/ecs2.3165

Grantham, N. S., Reich, B. J., Laber, E. B., Pacifici, K., Dunn, R. R., Fierer, N., … Faith, S. A. (2020). Global forensic geolocation with deep neural networks. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS. https://doi.org/10.1111/rssc.12427

Reich, B. J., Guan, Y., Fourches, D., Warren, J. L., Sarnat, S. E., & Chang, H. H. (2020). INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH. ANNALS OF APPLIED STATISTICS, 14(4), 1945–1963. https://doi.org/10.1214/20-AOAS1364

Wei, R., Reich, B. J., Hoppin, J. A., & Ghosal, S. (2020). SPARSE BAYESIAN ADDITIVE NONPARAMETRIC REGRESSION WITH APPLICATION TO HEALTH EFFECTS OF PESTICIDES MIXTURES. STATISTICA SINICA, 30(1), 55–79. https://doi.org/10.5705/ss.202017.0315

Winkel, M. A., Stallrich, J. W., Storlie, C. B., & Reich, B. J. (2020). Sequential Optimization in Locally Important Dimensions. TECHNOMETRICS. https://doi.org/10.1080/00401706.2020.1714738

Jhuang, A.-T., Fuentes, M., Bandyopadhyay, D., & Reich, B. J. (2020). Spatiotemporal signal detection using continuous shrinkage priors. STATISTICS IN MEDICINE. https://doi.org/10.1002/sim.8514

Majumder, S., Guan, Y., Reich, B. J., O'Neill, S., & Rappold, A. G. (2020). Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS. https://doi.org/10.1007/s13253-020-00420-4

Saia, S. M., Nelson, N., Huseth, A. S., Grieger, K., & Reich, B. J. (2020). Transitioning Machine Learning from Theory to Practice in Natural Resources Management. ECOLOGICAL MODELLING, 435. https://doi.org/10.1016/j.ecolmodel.2020.109257

Allwood, J. S., Fierer, N., Dunn, R. R., Breen, M., Reich, B. J., Laber, E. B., … Faith, S. A. (2020). Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance. FORENSIC SCIENCE INTERNATIONAL, 310. https://doi.org/10.1016/j.forsciint.2020.110250

Rekabdarkolaee, H. M., Krut, C., Fuentes, M., & Reich, B. J. (2019). A Bayesian multivariate functional model with spatially varying coefficient approach for modeling hurricane track data. SPATIAL STATISTICS, 29, 351–365. https://doi.org/10.1016/j.spasta.2018.12.006

Cloud, K. A., Reich, B. J., Rozoff, C. M., Alessandrini, S., Lewis, W. E., & Delle Monache, L. (2019). A Feed Forward Neural Network Based on Model Output Statistics for Short-Term Hurricane Intensity Prediction. WEATHER AND FORECASTING, 34(4), 985–997. https://doi.org/10.1175/WAF-D-18-0173.1

Reich, B. J., & Shaby, B. A. (2019). A Spatial Markov Model for Climate Extremes. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 28(1), 117–126. https://doi.org/10.1080/10618600.2018.1482764

Hazra, A., Reich, B. J., Reich, D. S., Shinohara, R. T., & Staicu, A.-M. (2019). A Spatio-Temporal Model for Longitudinal Image-on-Image Regression. STATISTICS IN BIOSCIENCES, 11(1), 22–46. https://doi.org/10.1007/s12561-017-9206-z

Sahoo, I., Guinness, J., & Reich, B. J. (2019). A TEST FOR ISOTROPY ON A SPHERE USING SPHERICAL HARMONIC FUNCTIONS. STATISTICA SINICA, 29(3), 1253–1276. https://doi.org/10.5705/ss.202017.0475

Hazra, A., Reich, B. J., & Staicu, A.-M. (2019). A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes. ENVIRONMETRICS. https://doi.org/10.1002/env.2602

Binion-Rock, S. M., Reich, B., & Buckel, J. A. (2019). A spatial kernel density method to estimate the diet composition of fish. CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES, 76(2), 249–267. https://doi.org/10.1139/cjfas-2017-0306

Guan, Q., Reich, B. J., Laber, E. B., & Bandyopadhyay, D. (2019). Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. https://doi.org/10.1080/01621459.2019.1660169

Huang, Y.-N., Reich, B. J., Fuentes, M., & Sankarasubramanian, A. (2019). COMPLETE SPATIAL MODEL CALIBRATION. ANNALS OF APPLIED STATISTICS, 13(2), 746–766. https://doi.org/10.1214/18-AOAS1219

Ferguson, A. L., Mueller, T., Rajasekaran, S., & Reich, B. J. (2019). Conference report: 2018 materials and data science hackathon (MATDAT18). MOLECULAR SYSTEMS DESIGN & ENGINEERING, Vol. 4, pp. 462–468. https://doi.org/10.1039/c9me90018g

Morris, S. A., Reich, B. J., & Thibaud, E. (2019). Exploration and Inference in Spatial Extremes Using Empirical Basis Functions. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 24(4), 555–572. https://doi.org/10.1007/s13253-019-00359-1

Guan, Y., Johnson, M. C., Katzfuss, M., Mannshardt, E., Messier, K. P., Reich, B. J., & Song, J. J. (2019). Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. https://doi.org/10.1080/01621459.2019.1665526

Hammerling, D., & Reich, B. J. (2019, September). Guest Editors' Introduction to the Special Issue on "Climate and the Earth System". JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, Vol. 24, pp. 395–397. https://doi.org/10.1007/s13253-019-00373-3

Grantham, N. S., Guan, Y., Reich, B. J., Borer, E. T., & Gross, K. (2019). MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. https://doi.org/10.1080/01621459.2019.1626242

Tsai, W.-L., Leung, Y.-F., McHale, M. R., Floyd, M. F., & Reich, B. J. (2019). Relationships between urban green land cover and human health at different spatial resolutions. URBAN ECOSYSTEMS, 22(2), 315–324. https://doi.org/10.1007/s11252-018-0813-3

Pacifici, K., Reich, B. J., Miller, D. A. W., & Pease, B. S. (2019). Resolving misaligned spatial data with integrated species distribution models. ECOLOGY, 100(6). https://doi.org/10.1002/ecy.2709

Jhuang, A.-T., Fuentes, M., Jones, J. L., Esteves, G., Fancher, C. M., Furman, M., & Reich, B. J. (2019). Spatial Signal Detection Using Continuous Shrinkage Priors. TECHNOMETRICS, 61(4), 494–506. https://doi.org/10.1080/00401706.2018.1546622

Miller, D. A. W., Pacifici, K., Sanderlin, J. S., & Reich, B. (2019). The recent past and promising future for data integration methods to estimate species' distributions. METHODS IN ECOLOGY AND EVOLUTION, 10(1), 22–37. https://doi.org/10.1111/2041-210X.13110

Jones, J. L., Broughton, R., Iamsasri, T., Fancher, C. M., Wilson, A. G., Reich, B., & Smith, R. C. (2019). The use of Bayesian inference in the characterization of materials and thin films. ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, Vol. 75, pp. A211–A211. https://doi.org/10.1107/S0108767319097940

King, M. C., Staicu, A.-M., Davis, J. M., Reich, B., & Eder, B. (2018). A functional data analysis of spatiotemporal trends and variation in fine particulate matter. Atmospheric Environment, 184, 233–243. https://doi.org/10.1016/j.atmosenv.2018.04.001

Libera, D. A., Sankarasubramanian, A., Sharma, A., & Reich, B. (2018). A non-parametric bootstrapping framework embedded in a toolkit for assessing water quality model performance. ENVIRONMENTAL MODELLING & SOFTWARE, 107, 25–33. https://doi.org/10.1016/j.envsoft.2018.05.013

Irizarry, A. D., Collazo, J. A., Pacifici, K., Reich, B., & Battle, K. E. (2018). Avian response to shade-layer restoration in coffee plantations in Puerto Rico. RESTORATION ECOLOGY, 26(6), 1212–1220. https://doi.org/10.1111/rec.12697

Reich, B., Guinness, J., Vandekar, S. N., Shinohara, R. T., & Staicu, A.-M. (2018). Fully Bayesian spectral methods for imaging data. BIOMETRICS, 74(2), 645–652. https://doi.org/10.1111/biom.12782

Larsen, A. E., Reich, B., Ruminski, M., & Rappold, A. G. (2018). Impacts of fire smoke plumes on regional air quality, 2006-2013. Journal of Exposure Science and Environmental Epidemiology, 28(4), 319–327. https://doi.org/10.1038/s41370-017-0013-x

Reich, B., Pacifici, K., & Stallings, J. W. (2018). Integrating auxiliary data in optimal spatial design for species distribution modelling. Methods in Ecology and Evolution, 9(6), 1626–1637. https://doi.org/10.1111/2041-210x.13002

Reich, B. J., & Haran, M. (2018). Precision maps for public health. Nature, 555(7694), 32–33. https://doi.org/10.1038/D41586-018-02096-W

Kang, J., Reich, B., & Staicu, A.-M. (2018). Scalar-on-image regression via the soft-thresholded Gaussian process. Biometrika, 105(1), 165–184. https://doi.org/10.1093/biomet/asx075

Grantham, N. S., Reich, B. J., Liu, Y., & Chang, H. H. (2018). Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter. Environmetrics, 29(4), e2499. https://doi.org/10.1002/ENV.2499

Janko, M. M., Irish, S. R., Reich, B. J., Peterson, M., Doctor, S. M., Mwandagalirwa, M. K., … Emch, M. E. (2018). The links between agriculture, Anopheles mosquitoes, and malaria risk in children younger than 5 years in the Democratic Republic of the Congo: a population-based, cross-sectional, spatial study. The Lancet Planetary Health, 2(2), e74–e82. https://doi.org/10.1016/S2542-5196(18)30009-3

Li, Q. W., Guindani, M., Reich, B., Bondell, H. D., & Vannucci, M. (2017). A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints. Statistical Analysis and Data Mining, 10(6), 393–409. https://doi.org/10.1002/sam.11350

Kaufeld, K. A., Fuentes, M., Reich, B. J., Herring, A. H., Shaw, G. M., & Terres, M. A. (2017). A multivariate dynamic spatial factor model for speciated pollutants and adverse birth outcomes. International Journal of Environmental Research and Public Health, 14(9).

Morris, S. A., Reich, B., Thibaud, E., & Cooley, D. (2017). A space-time skew-t model for threshold exceedances. Biometrics, 73(3), 749–758. https://doi.org/10.1111/biom.12644

Morris, S. A., Reich, B., Pacifici, K., & Lei, Y. C. (2017). A spatial model for rare binary events. Environmental and Ecological Statistics, 24(4), 485–504. https://doi.org/10.1007/s10651-017-0385-z

Wootten, A., Terando, A., Reich, B., Boyles, R. P., & Semazzi, F. (2017). Characterizing sources of uncertainty from global climate models and downscaling techniques. Journal of Applied Meteorology and Climatology, 56(12), 3245–3262. https://doi.org/10.1175/jamc-d-17-0087.1

Wilson, A., Reich, B., Nolte, C. G., Spero, T. L., Hubbell, B., & Rappold, A. G. (2017). Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone-temperature risk surfaces. Journal of Exposure Science and Environmental Epidemiology, 27(1), 118–124. https://doi.org/10.1038/jes.2016.14

Cabral, M. J., Zhang, S., Chi, J., Reich, B. J., Dickey, E. C., & LeBeau, J. M. (2017). Correlating Local Chemistry and Local Cation Displacements in the Relaxor Ferroelectric PMN. Microscopy and Microanalysis, 23(S1), 1616–1617. https://doi.org/10.1017/S1431927617008741

Li, D., Bucholz, E. W., Peterson, G., Reich, B. J., Russ, J. C., & Brenner, D. W. (2017). How predictable is plastic damage at the atomic scale? Applied Physics Letters, 110(9), 091902. https://doi.org/10.1063/1.4977420

Pacifici, K., Reich, B., Miller, D. A. W., Gardner, B., Stauffer, G., Singh, S., … Collazo, J. A. (2017). Integrating multiple data sources in species distribution modeling: a framework for data fusion. Ecology, 98(3), 840–850. https://doi.org/10.1002/ecy.1710

Farjat, A., Reich, B., Guinness, J., Whetten, R., McKeand, S., & Isik, F. (2017). Optimal seed deployment under climate change using spatial models: Application to loblolly pine in the Southeastern US. Journal of the American Statistical Association, 112(519), 909–920. https://doi.org/10.1080/01621459.2017.1292179

Peterson, G. C. L., Li, D., Reich, B., & Brenner, D. (2017). Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage. Journal of Applied Statistics, 44(10), 1761–1784. https://doi.org/10.1080/02664763.2016.1221915

Li, D., Reich, B., & Brenner, D. W. (2017). Statistical and image analysis for characterizing simulated atomic-scale damage in crystals. Computational Materials Science, 135, 119–126. https://doi.org/10.1016/j.commatsci.2017.03.054

Terando, A. J., Reich, B., Pacifici, K., Costanza, J., McKerrow, A., & Collazo, J. A. (2017). Uncertainty Quantification and Propagation for Projections of Extremes in Monthly Area Burned Under Climate Change: A Case Study in the Coastal Plain of Georgia, USA. In NATURAL HAZARD UNCERTAINTY ASSESSMENT: MODELING AND DECISION SUPPORT (Vol. 223, pp. 245–256). https://doi.org/10.1002/9781119028116.ch16

Li, D., Reich, B. J., & Brenner, D. W. (2017). Using spatial cross-correlation image analysis to characterize the influence of strain rate on plastic damage in molecular dynamics simulations. Modelling and Simulation in Materials Science and Engineering, 25(7).

Storlie, C. B., Reich, B., Rust, W. N., Ticknor, L. O., Bonnie, A. M., Montoya, A. J., & Michalak, S. E. (2017). spatiotemporal modeling of node temperatures in supercomputers. Journal of the American Statistical Association, 112(517), 92–108. https://doi.org/10.1080/01621459.2016.1195271

Shaby, B. A., Reich, B., Cooley, D., & Kaufman, C. G. (2016). A Markov-switching model for heat waves. Annals of Applied Statistics, 10(1), 74–93. https://doi.org/10.1214/15-aoas873

Balderama, E., Gardner, B., & Reich, B. (2016). A Spatial-temporal double-hurdle model for extremely over-dispersed avian count data. Spatial Statistics, 18, 263–275. https://doi.org/10.1016/j.spasta.2016.05.001

Parker, R. J., Reich, B., & Eidsvik, J. (2016). A fused lasso approach to nonstationary spatial covariance estimation. Journal of Agricultural Biological and Environmental Statistics, 21(3), 569–587. https://doi.org/10.1007/s13253-016-0251-8

Guan, Q., Laber, E. B., & Reich, B. J. (2016). Bayesian nonparametric estimation for dynamic treatment regimes with sequential transition times comment. Journal of the American Statistical Association, 111(515), 936–942.

Guan, Q., Laber, E. B., & Reich, B. J. (2016). Comment. Journal of the American Statistical Association, 111(515), 936–942. https://doi.org/10.1080/01621459.2016.1200911

Russell, B. T., Cooley, D. S., Porter, W. C., Reich, B., & Heald, C. L. (2016). Data mining to investigate the meteorological drivers for extreme ground level ozone events. Annals of Applied Statistics, 10(3), 1673–1698. https://doi.org/10.1214/16-aoas954

Pacifici, K., Reich, B., Dorazio, R. M., & Conroy, M. J. (2016). Occupancy estimation for rare species using a spatially-adaptive sampling design. Methods in Ecology and Evolution, 7(3), 285–293. https://doi.org/10.1111/2041-210x.12499

Reich, B. J. (2016). Quantile regression for epidemiological applications. Handbook of Spatial Epidemiology, 239–249.

Tsai, W. L., Floyd, M. F., Leung, Y.-F., McHale, M. R., & Reich, B. (2016). Urban Vegetative Cover Fragmentation in the US Associations With Physical Activity and BMI. American Journal of Preventive Medicine, 50(4), 509–517. https://doi.org/10.1016/j.amepre.2015.09.022

Fancher, C. M., Han, Z., Levin, I., Page, K., Reich, B. J., Smith, R. C., … Jones, J. L. (2016). Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis. Scientific Reports, 6(1). https://doi.org/10.1038/SREP31625

Schnell, P., Bandyopadhyay, D., Reich, B., & Nunn, M. (2015). A marginal cure rate proportional hazards model for spatial survival data. Journal of the Royal Statistical Society. Series C, Applied Statistics, 64(4), 673–691. https://doi.org/10.1111/rssc.12098

Parker, R. J., Reich, B., & Sain, S. R. (2015). A multiresolution approach to estimating the value added by regional climate models. Journal of Climate, 28(22), 8873–8887. https://doi.org/10.1175/jcli-d-14-00557.1

Chang, H. H., Warren, J. L., Darrow, L. A., Reich, B., & Waller, L. A. (2015). Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study. Biostatistics (Oxford, England), 16(3), 509–521. https://doi.org/10.1093/biostatistics/kxu060

Stephenson, A. G., Shaby, B. A., Reich, B., & Sullivan, A. L. (2015). Estimating spatially varying severity thresholds of a forest fire danger rating system using max-stable extreme-event modeling. Journal of Applied Meteorology and Climatology, 54(2), 395–407. https://doi.org/10.1175/jamc-d-14-0041.1

Sun, W. G., Reich, B., Cai, T. T., Guindani, M., & Schwartzman, A. (2015). False discovery control in large-scale spatial multiple testing. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 77(1), 59–83. https://doi.org/10.1111/rssb.12064

Grantham, N. S., Reich, B., Pacifici, K., Laber, E. B., Menninger, H. L., Henley, J. B., … Dunn, R. (2015). Fungi identify the geographic origin of dust samples. PLoS One, 10(4). https://doi.org/10.1371/journal.pone.0122605

Kao, Y. M., Reich, B., Storlie, C., & Anderson, B. (2015). Malware detection using nonparametric bayesian clustering and classification techniques. Technometrics, 57(4), 535–546. https://doi.org/10.1080/00401706.2014.958916

Farjat, A. E., Isik, F., Reich, B., Whetten, R., & McKeand, S. (2015). Modeling climate change effects on the height growth of loblolly pine. Forest Science, 61(4), 703–715. https://doi.org/10.5849/forsci.14-075

Smith, L. B., Reich, B., Herring, A. H., Langlois, P. H., & Fuentes, M. (2015). Multilevel quantile function modeling with application to birth outcomes. Biometrics, 71(2), 508–519. https://doi.org/10.1111/biom.12294

Coleman, D. A., Martin, D. E. K., & Reich, B. (2015). Multiple window discrete scan statistic for higher-order Markovian sequences. Journal of Applied Statistics, 42(8), 1690–1705. https://doi.org/10.1080/02664763.2015.1005061

Reich, B., & Porter, M. D. (2015). Partially supervised spatiotemporal clustering for burglary crime series identification. Journal of the Royal Statistical Society Series A-Statistics in Society, 178(2), 465–480. https://doi.org/10.1111/rssa.12076

Smith, L. B., Fuentes, M., Gordon-Larsen, P., & Reich, B. (2015). Quantile regression for mixed models with an application to examine blood pressure trends in China. Annals of Applied Statistics, 9(3), 1226–1246. https://doi.org/10.1214/15-aoas841

Reich, B., & Fuentes, M. (2015). Spatial Bayesian nonparametric methods. Nonparametric Bayesian Inference in Biostatistics, 347–357. https://doi.org/10.1007/978-3-319-19518-6_17

Vock, L. F. B., Reich, B., Fuentes, M., & Dominici, F. (2015). Spatial variable selection methods for investigating acute health effects of fine particulate matter components. Biometrics, 71(1), 167–177. https://doi.org/10.1111/biom.12254

Reich, B., Shaby, B. A., & Cooley, D. (2014). A hierarchical model for serially-dependent extremes: A study of heat waves in the western US. Journal of Agricultural Biological and Environmental Statistics, 19(1), 119–135. https://doi.org/10.1007/s13253-013-0161-y

Reich, B., & Gardner, B. (2014). A spatial capture-recapture model for territorial species. Environmetrics, 25(8), 630–637. https://doi.org/10.1002/env.2317

Reich, B., Chang, H. H., & Foley, K. M. (2014). A spectral method for spatial downscaling. Biometrics, 70(4), 932–942. https://doi.org/10.1111/biom.12196

Wilson, A., & Reich, B. (2014). Confounder selection via penalized credible regions. Biometrics, 70(4), 852–861. https://doi.org/10.1111/biom.12203

Eidsvik, J., Shaby, B. A., Reich, B., Wheeler, M., & Niemi, J. (2014). Estimation and prediction in spatial models with block composite likelihoods. Journal of Computational and Graphical Statistics, 23(2), 295–315. https://doi.org/10.1080/10618600.2012.760460

Wilson, A., Reif, D. M., & Reich, B. (2014). Hierarchical dose-response modeling for high-throughput toxicity screening of environmental chemicals. Biometrics, 70(1), 237–246. https://doi.org/10.1111/biom.12114

Wilson, A., Rappold, A. G., Neas, L. M., & Reich, B. (2014). Modeling the effect of temperature on ozone-related mortality. Annals of Applied Statistics, 8(3), 1728–1749. https://doi.org/10.1214/14-aoas754

Reich, B., Chang, H. H., & Strickland, M. J. (2014). Spatial health effects analysis with uncertain residential locations. Statistical Methods in Medical Research, 23(2), 156–168. https://doi.org/10.1177/0962280212447151

Wang, H., Reich, B., & Lim, Y. H. (2013). A Bayesian approach to probabilistic streamflow forecasts. Journal of Hydroinformatics, 15(2), 381–391. https://doi.org/10.2166/hydro.2012.080

Reich, B., Bandyopadhyay, D., & Bondell, H. D. (2013). A nonparametric spatial model for periodontal data with nonrandom missingness. Journal of the American Statistical Association, 108(503), 820–831. https://doi.org/10.1080/01621459.2013.795487

Chang, H. H., Reich, B. J., & Miranda, M. L. (2013). A spatial time-to-event approach for estimating associations between air pollution and preterm birth. Journal of the Royal Statistical Society. Series C, Applied Statistics, 62, 167–179.

Storlie, C. B., Reich, B., Helton, J. C., Swiler, L. P., & Sallaberry, C. J. (2013). Analysis of computationally demanding models with continuous and categorical inputs. Reliability Engineering & System Safety, 113, 30–41. https://doi.org/10.1016/j.ress.2012.11.018

Reich, B., & Smith, L. B. (2013). Bayesian quantile regression for censored data. Biometrics, 69(3), 651–660. https://doi.org/10.1111/biom.12053

Boehm, L., Reich, B., & Bandyopadhyay, D. (2013). Bridging conditional and marginal inference for spatially referenced binary data. Biometrics, 69(2), 545–554. https://doi.org/10.1111/biom.12027

Mannshardt, E., Sucic, K., Jiao, W., Dominici, F., Frey, H. C., Reich, B., & Fuentes, M. (2013). Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions. Journal of Exposure Science and Environmental Epidemiology, 23(6), 627–636. https://doi.org/10.1038/jes.2013.39

Reich, B., & Porter, M. D. (2013). Discussion of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard. Annals of Applied Statistics, 7(4), 1871–1875. https://doi.org/10.1214/13-aoas614b

Reich, B., Cooley, D., Foley, K., Napelenok, S., & Shaby, B. (2013). Extreme value analysis for evaluating ozone control strategies. Annals of Applied Statistics, 7(2), 739–762. https://doi.org/10.1214/13-aoas628

Fuentes, M., & Reich, B. (2013). Multivariate spatial nonparametric modelling via kernel processes mixing. Statistica Sinica, 23(1), 75–97.

Fuentes, M., Henry, J., & Reich, B. (2013). Nonparametric spatial models for extremes: application to extreme temperature data. Extremes, 16(1), 75–101. https://doi.org/10.1007/s10687-012-0154-1

Reich, B., & Shaby, B. A. (2012). A hierarchical max-stable spatial model for extreme precipitation. Annals of Applied Statistics, 6(4), 1430–1451. https://doi.org/10.1214/12-aoas591

Foley, K. M., Reich, B., & Napelenok, S. L. (2012). Bayesian analysis of a reduced-form air quality model. Environmental Science & Technology, 46(14), 7604–7611. https://doi.org/10.1021/es300666e

Shaby, B. A., & Reich, B. (2012). Bayesian spatial extreme value analysis to assess the changing risk of concurrent high temperatures across large portions of European cropland. Environmetrics, 23(8), 638–648. https://doi.org/10.1002/env.2178

Chang, H. H., Reich, B., & Miranda, M. L. (2012). Chang et al. Respond to "Environmental Exposures and Preterm Birth". American Journal of Epidemiology, 175(2), 111–112. https://doi.org/10.1093/aje/kwr406

Modlin, D., Fuentes, M., & Reich, B. (2012). Circular conditional autoregressive modeling of vector fields. Environmetrics, 23(1), 46–53. https://doi.org/10.1002/env.1133

Bondell, H. D., & Reich, B. (2012). Consistent high-dimensional Bayesian variable selection via penalized credible regions. Journal of the American Statistical Association, 107(500), 1610–1624. https://doi.org/10.1080/01621459.2012.716344

Cooley, D., Sain, S. R., Gabda, D., Towe, R., Wadsworth, J., Tawn, J., … Ribatet, M. (2012). Discussion of "Statistical modeling of spatial extremes" by A. C. Davison, S. A. Padoan and M. Ribatet. Statistical Science, 27(2), 187–201.

Porter, M. D., & Reich, B. J. (2012). Evaluating temporally weighted kernel density methods for predicting the next event location in a series. Annals of GIS, 18(3), 225–240. https://doi.org/10.1080/19475683.2012.691904

Hayashi, K., Hayashi, M., Reich, B., Lee, S.-P., Sachdeva, A. U. C., & Mizoguchi, I. (2012). Functional data analysis of mandibular movement using third-degree b-spline basis functions and self-modeling regression. Orthodontic Waves, 71(1), 17–25. https://doi.org/10.1016/j.odw.2011.11.001

Reich, B., & Fuentes, M. (2012). Nonparametric Bayesian models for a spatial covariance. Statistical Methodology, 9(1-2), 265–274. https://doi.org/10.1016/j.stamet.2011.01.007

Reich, B. J. (2012). Spatiotemporal quantile regression for detecting distributional changes in environmental processes. Journal of the Royal Statistical Society. Series C, Applied Statistics, 61, 535–553.

Reich, B. J., Kalendra, E., Storlie, C. B., Bondell, H. D., & Fuentes, M. (2012). Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation. Journal of the Royal Statistical Society. Series C, Applied Statistics, 61, 47–66.

Bandyopadhyay, D., Reich, B., & Slate, E. H. (2011). A spatial beta-binomial model for clustered count data on dental caries. Statistical Methods in Medical Research, 20(2), 85–102. https://doi.org/10.1177/0962280210372453

Reich, B., & Bondell, H. D. (2011). A spatial dirichlet process mixture model for clustering population genetics data. Biometrics, 67(2), 381–390. https://doi.org/10.1111/j.1541-0420.2010.01484.x

Pati, D., Reich, B., & Dunson, D. B. (2011). Bayesian geostatistical modelling with informative sampling locations. Biometrika, 98(1), 35–48. https://doi.org/10.1093/biomet/asq067

Reich, B., Fuentes, M., & Dunson, D. B. (2011). Bayesian spatial quantile regression. Journal of the American Statistical Association, 106(493), 6–20. https://doi.org/10.1198/jasa.2010.ap09237

Reich, B., & Haran, M. (2011). Guest editors' introduction to the special issue on "computer models and spatial statistics for environmental science". Journal of Agricultural Biological and Environmental Statistics, 16(4), 451–452. https://doi.org/10.1007/s13253-011-0071-9

Havard, S., Reich, B., Bean, K., & Chaix, B. (2011). Social inequalities in residential exposure to road traffic noise: An environmental justice analysis based on the RECORD Cohort Study. Occupational and Environmental Medicine, 68(5), 366–374. https://doi.org/10.1136/oem.2010.060640

Reich, B., Bondell, H. D., & Li, L. X. (2011). Sufficient dimension reduction via Bayesian mixture modeling. Biometrics, 67(3), 886–895. https://doi.org/10.1111/j.1541-0420.2010.01501.x

Storlie, C. B., Bondell, H. D., Reich, B. J., & Zhang, H. H. (2011). Surface estimation, variable selection, and the nonparametric oracle property. Statistica Sinica, 21(2), 679–705.

Reich, B., & Bandyopadhyay, D. (2010). A latent factor model for spatial data with informative missingness. Annals of Applied Statistics, 4(1), 439–459. https://doi.org/10.1214/09-aoas278

Storlie, C. B., Bondell, H. D., & Reich, B. (2010). A locally adaptive penalty for estimation of functions with varying roughness. Journal of Computational and Graphical Statistics, 19(3), 569–589. https://doi.org/10.1198/jcgs.2010.09020

Hodges, J. S., & Reich, B. (2010). Adding spatially-correlated errors can mess up the fixed effect you love. American Statistician, 64(4), 325–334. https://doi.org/10.1198/tast.2010.10052

Reich, B., Fuentes, M., Herring, A. H., & Evenson, K. R. (2010). Bayesian variable selection for multivariate spatially varying coefficient regression. Biometrics, 66(3), 772–782. https://doi.org/10.1111/j.1541-0420.2009.01333.x

Hayashi, K., Mizoguchi, I., Lee, S. P., & Reich, B. (2010). Development of a novel statistical model for mandibular kinematics. Medical Engineering & Physics, 32(5), 423–428. https://doi.org/10.1016/j.medengphy.2010.04.005

Reich, B., Bondell, H. D., & Wang, H. J. (2010). Flexible Bayesian quantile regression for independent and clustered data. Biostatistics (Oxford, England), 11(2), 337–352. https://doi.org/10.1093/biostatistics/kxp049

Bondell, H. D., Reich, B., & Wang, H. X. (2010). Noncrossing quantile regression curve estimation. Biometrika, 97(4), 825–838. https://doi.org/10.1093/biomet/asq048

Hayashi, K., Reich, B., Delong, R., Lee, S. P., & Mizoguchi, I. (2009). A novel statistical model for mandibular helical axis analysis. Journal of Oral Rehabilitation, 36(2), 102–109. https://doi.org/10.1111/j.1365-2842.2008.01890.x

Reich, B., Fuentes, M., & Burke, J. (2009). Analysis of the effects of ultrafine particulate matter while accounting for human exposure. Environmetrics, 20(2), 131–146. https://doi.org/10.1002/env.915

Bandyopadhyay, D., Reich, B., & Slate, E. H. (2009). Bayesian modeling of multivariate spatial binary data with applications to dental caries. Statistics in Medicine, 28(28), 3492–3508. https://doi.org/10.1002/sim.3647

Costalonga, M., Batas, L., & Reich, B. (2009). Effects of toll-like receptor 4 on Porphyromonas gingivalis-induced bone loss in mice. Journal of Periodontal Research, 44(4), 537–542. https://doi.org/10.1111/j.1600-0765.2008.01152.x

Choi, J., Reich, B. J., Fuentes, M., & Davis, J. M. (2009). Multivariate Spatial-Temporal Modeling and Prediction of Speciated Fine Particles. Journal of Statistical Theory and Practice, 3(2), 407–418. https://doi.org/10.1080/15598608.2009.10411933

Bondell, H. D., & Reich, B. (2009). Simultaneous factor selection and collapsing levels in ANOVA. Biometrics, 65(1), 169–177. https://doi.org/10.1111/j.1541-0420.2008.01061.x

Choi, J., Fuentes, M., & Reich, B. (2009). Spatial-temporal association between fine particulate matter and daily mortality. Computational Statistics & Data Analysis, 53(8), 2989–3000. https://doi.org/10.1016/j.csda.2008.05.018

Reich, B., Storlie, C. B., & Bondell, H. D. (2009). Variable selection in Bayesian smoothing spline ANOVA models: Application to deterministic computer codes. Technometrics, 51(2), 110–120. https://doi.org/10.1198/TECH.2009.0013

Reich, B., & Hodges, J. S. (2008). Identitication of the variance components in the general two-variance linear model. Journal of Statistical Planning and Inference, 138(6), 1592–1604. https://doi.org/10.1016/j.jspi.2007.05.046

Reich, B., & Hodges, J. S. (2008). Modeling longitudinal spatial periodontal data: A spatially adaptive model with tools for specifying priors and checking fit. Biometrics, 64(3), 790–799. https://doi.org/10.1111/j.1541-0420.2007.00956.x

Bondell, H. D., & Reich, B. (2008). Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Biometrics, 64(1), 115–123. https://doi.org/10.1111/j.1541-0420.2007.00843.x

Fuentes, M., Reich, B., & Lee, G. (2008). Spatial-temporal mesoscale modeling of rainfall intensity using gage and radar data. Annals of Applied Statistics, 2(4), 1148–1169. https://doi.org/10.1214/08-AOAS166

Reich, B., & Fuentes, M. (2007). A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields. Annals of Applied Statistics, 1(1), 249–264. https://doi.org/10.1214/07-AOAS108

Reich, B., Hodges, J. S., Carlin, B. P., & Reich, A. M. (2006). A spatial analysis of basketball shot chart data. American Statistician, 60(1), 3–12. https://doi.org/10.1198/000313006X90305

Reich, B., Hodges, J. S., & Zadnik, V. (2006). Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models. Biometrics, 62(4), 1197–1206. https://doi.org/10.1111/j.1541-0420.2006.00617

Lemmonds, C. A., Mooney, M., Reich, B., & Hatsukami, D. (2004). Characteristics of cigarette smokers seeking treatment for cessation versus reduction. Addictive Behaviors, 29(2), 357–364. https://doi.org/10.1016/j.addbeh.2003.08.049

Allen, S. S., Brintnell, D. M., Hatsukami, D., & Reich, B. (2004). Energy intake and physical activity during short-term smoking cessation in postmenopausal women. Addictive Behaviors, 29(5), 947–951. https://doi.org/10.1016/j.addbeh.2004.02.041