Brian Reich

Majumder, R., Terando, A. J., Hiers, J. K., Collazo, J. A., & Reich, B. J. (2025). A spatiotemporal optimization engine for prescribed burning in the Southeast US. ECOLOGICAL INFORMATICS, 85. https://doi.org/10.1016/j.ecoinf.2024.102956

Hector, E. C., Reich, B. J., & Eloyan, A. (2025). Distributed model building and recursive integration for big spatial data modeling. BIOMETRICS, 81(1). https://doi.org/10.1093/biomtc/ujae159

Trostle, P., Guinness, J., & Reich, B. J. (2024). A Gaussian-process approximation to a spatial SIR process using moment closures and emulators. BIOMETRICS, 80(3). https://doi.org/10.1093/biomtc/ujae068

Gill, D. A., Lester, S. E., Free, C. M., Pfaff, A., Iversen, E., Reich, B. J., … Warmuth, L. M. (2024). A diverse portfolio of marine protected areas can better advance global conservation and equity. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 121(10). https://doi.org/10.1073/pnas.2313205121

Zulian, V., Pacifici, K., Bacheler, N. M., Buckel, J. A., Patterson Iii, W. F., Reich, B. J., … Hostetter, N. J. (2024, December 6). Applying mark-resight, count, and telemetry data to estimate effective sampling area and fish density with stationary underwater cameras. CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES, Vol. 12. https://doi.org/10.1139/cjfas-2023-0373

Chen, W., Li, Y., Reich, B. J., & Sun, Y. (2024). DEEPKRIGING: SPATIALLY DEPENDENT DEEP NEURAL NETWORKS FOR SPATIAL PREDICTION. STATISTICA SINICA, 34(1), 291–311. https://doi.org/10.5705/ss.202021.0277

Wu, L., Gao, C., Yang, S., Reich, B. J., & Rappold, A. G. (2024, July 16). Estimating spatially varying health effects of wildland fire smoke using mobile health data. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol. 7. https://doi.org/10.1093/jrsssc/qlae034

Majumder, R., Reich, B. J., & Shaby, B. A. (2024). MODELING EXTREMAL STREAMFLOW USING DEEP LEARNING APPROXIMATIONS AND A FLEXIBLE SPATIAL PROCESS. ANNALS OF APPLIED STATISTICS, 18(2), 1519–1542. https://doi.org/10.1214/23-AOAS1847

Kotlarz, N., McCord, J., Wiecha, N., Weed, R. A., Cuffney, M., Enders, J. R., … Hoppin, J. A. (2024, February). Measurement of Hydro-EVE and 6:2 FTS in Blood from Wilmington, North Carolina, Residents, 2017-2018. ENVIRONMENTAL HEALTH PERSPECTIVES, Vol. 132. https://doi.org/10.1289/EHP14503

Simafranca, N., Willoughby, B., O'Neil, E., Farr, S., Reich, B. J., Giertych, N., … Pascolini-Campbell, M. A. (2024, March 22). Modeling wildland fire burn severity in California using a spatial Super Learner approach. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, Vol. 3. https://doi.org/10.1007/s10651-024-00601-1

Kotlarz, N., McCord, J., Wiecha, N., Weed, R. A., Cuffney, M., Enders, J. R., … Hoppin, J. A. (2024, February). Reanalysis of PFO5DoA Levels in Blood from Wilmington, North Carolina, Residents, 2017-2018. ENVIRONMENTAL HEALTH PERSPECTIVES, Vol. 132. https://doi.org/10.1289/EHP13339

Euan, C., Sun, Y., & Reich, B. J. (2024). Regime-based precipitation modeling: A spatio-temporal approach. SPATIAL STATISTICS, 60. https://doi.org/10.1016/j.spasta.2024.100818

Yanchenko, E., Bondell, H. D., & Reich, B. J. (2024, May 23). The R2D2 Prior for Generalized Linear Mixed Models. AMERICAN STATISTICIAN, Vol. 5. https://doi.org/10.1080/00031305.2024.2352010

Yang, H., Ruiz-Suarez, S., Reich, B. J., Guan, Y., & Rappold, A. G. (2023). A Data-Fusion Approach to Assessing the Contribution of Wildland Fire Smoke to Fine Particulate Matter in California. REMOTE SENSING, 15(17). https://doi.org/10.3390/rs15174246

Long, A. S., Reich, B. J., Staicu, A.-M., & Meitzen, J. (2023). A Nonparametric Test of Group Distributional Differences for Hierarchically Clustered Functional Data. Biometrics, 79(4), 3778–3791. https://doi.org/10.1111/biom.13846

Abba, M. A., Williams, J. P., & Reich, B. J. (2023). A PENALIZED COMPLEXITY PRIOR FOR DEEP BAYESIAN TRANSFER LEARNING WITH APPLICATION TO MATERIALS INFORMATICS. ANNALS OF APPLIED STATISTICS, 17(4), 3241–3256. https://doi.org/10.1214/23-AOAS1759

Werthmann, D., Joode, B. van W., Cuffney, M. T., Reich, B. J., Soto-Martinez, M. E., Corrales-Vargas, A., … Hoppin, J. A. (2023). A cross-sectional analysis of medical conditions and environmental factors associated with fractional exhaled nitric oxide (FeNO) in women and children from the ISA birth cohort, Costa Rica. ENVIRONMENTAL RESEARCH, 233. https://doi.org/10.1016/j.envres.2023.116449

Majumder, R., & Reich, B. J. (2023). A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting. SPATIAL STATISTICS, 55. https://doi.org/10.1016/j.spasta.2023.100755

Awasthi, C., Archfield, S. A., Reich, B. J., & Sankarasubramanian, A. (2023). Beyond Simple Trend Tests: Detecting Significant Changes in Design-Flood Quantiles. GEOPHYSICAL RESEARCH LETTERS, 50(13). https://doi.org/10.1029/2023GL103438

Burgener, L., Hyland, E., Reich, B. J., & Scotese, C. (2023). Cretaceous climates: Mapping paleo-Koppen climatic zones using a Bayesian statistical analysis of lithologic, paleontologic, and geochemical proxies. PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY, 613. https://doi.org/10.1016/j.palaeo.2022.111373

Hector, E. C., & Reich, B. J. (2023, April 12). Distributed Inference for Spatial Extremes Modeling in High Dimensions. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 4. https://doi.org/10.1080/01621459.2023.2186886

Sahoo, I., Guinness, J., & Reich, B. J. J. (2023, July 6). Estimating atmospheric motion winds from satellite image data using space-time drift models. ENVIRONMETRICS, Vol. 7. https://doi.org/10.1002/env.2818

Mohottige, D., Davenport, C. A., Bhavsar, N., Schappe, T., Lyn, M. J., Maxson, P., … Boulware, L. E. (2023). Residential Structural Racism and Prevalence of Chronic Health Conditions. JAMA NETWORK OPEN, 6(12). https://doi.org/10.1001/jamanetworkopen.2023.48914

Yanchenko, E., Bondell, H. D., & Reich, B. J. (2023, October 27). Spatial regression modeling via the R2D2 framework. ENVIRONMETRICS, Vol. 10. https://doi.org/10.1002/env.2829

Nag, P., Sun, Y., & Reich, B. J. (2023). Spatio-temporal DeepKriging for interpolation and probabilistic forecasting. SPATIAL STATISTICS, 57. https://doi.org/10.1016/j.spasta.2023.100773

Larsen, A., Yang, S., Reich, B. J., & Rappold, A. G. (2022). A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM2.5 USING NUMERICAL MODEL OUTPUT. ANNALS OF APPLIED STATISTICS, 16(4), 2714–2731. https://doi.org/10.1214/22-AOAS1610

Trostle, P., Corzo, C. A., Reich, B. J., & Machado, G. (2022, October 29). A discrete-time survival model for porcine epidemic diarrhoea virus. TRANSBOUNDARY AND EMERGING DISEASES, Vol. 10. https://doi.org/10.1111/tbed.14739

Zhang, Y. D., Naughton, B. P., Bondell, H. D., & Reich, B. J. (2022). Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 117(538), 862–874. https://doi.org/10.1080/01621459.2020.1825449

Miller, M. J. J., & Reich, B. J. J. (2022). Bayesian spatial modeling using random Fourier frequencies. SPATIAL STATISTICS, 48. 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

Giffin, A., Gong, W., Majumder, S., Rappold, A. G., Reich, B. J., & Yang, S. (2022). Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread. SPATIAL STATISTICS, 52. https://doi.org/10.1016/j.spasta.2022.100711

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, 810. https://doi.org/10.1016/j.scitotenv.2021.151288

Giffin, A., Reich, B. J., Yang, S., & Rappold, A. G. (2022, September 19). Generalized propensity score approach to causal inference with spatial interference. BIOMETRICS, Vol. 9. https://doi.org/10.1111/biom.13745

Lan, Z., Reich, B. J., Guinness, J., Bandyopadhyay, D., Ma, L., & Moeller, F. G. (2022). Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging. BIOMETRICS, 78(2), 548–559. https://doi.org/10.1111/biom.13445

Majumder, S., Guan, Y., Reich, B. J., & Saibaba, A. K. (2022). Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods. STATISTICS AND COMPUTING, 32(5). https://doi.org/10.1007/s11222-022-10104-3

Huberman, D. B., Reich, B. J., & Bondell, H. D. (2022, August 26). Nonparametric conditional density estimation in a deep learning framework for short-term forecasting (May, 10.1007/s10651-021-00499-z, 2021). ENVIRONMENTAL AND ECOLOGICAL STATISTICS, Vol. 8. https://doi.org/10.1007/s10651-022-00543-6

Giffin, A., Hoppin, J. A., Cordoba, L., Solano-Diaz, K., Ruepert, C., Penaloza-Castaneda, J., … Joode, B. van W. (2022). Pyrimethanil and chlorpyrifos air concentrations and pregnant women's urinary metabolites in the Infants' Environmental Health Study (ISA) Costa Rica , ENVIRONMENT INTERNATIONAL, 166. https://doi.org/10.1016/j.envint.2022.107328

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

Guan, Y., Page, G. L., Reich, B. J., Ventrucci, M., & Yang, S. (2022, December 21). Spectral adjustment for spatial confounding. BIOMETRIKA, Vol. 12. https://doi.org/10.1093/biomet/asac069

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

Mao, H., Martin, R., & Reich, B. J. J. (2022, December 17). Valid Model-Free Spatial Prediction. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 12. https://doi.org/10.1080/01621459.2022.2147531

Pease, B. S., Pacifici, K., Kays, R., & Reich, B. (2022, July 4). What drives spatially varying ecological relationships in a wide-ranging species? DIVERSITY AND DISTRIBUTIONS, Vol. 7. https://doi.org/10.1111/ddi.13594

Tian, Y., & Reich, B. J. (2021). A BAYESIAN SEMI-PARAMETRIC MIXTURE MODEL FOR BIVARIATE EXTREME VALUE ANALYSIS WITH APPLICATION TO PRECIPITATION FORECASTING. STATISTICA SINICA, 31(3), 1619–1641. https://doi.org/10.5705/ss.202018.0420

Reich, B., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J., & Rappold, A. (2021). 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, 49(1), 129–149. 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

Dorman, S. J., Hopperstad, K. A., Reich, B. J., Majumder, S., Kennedy, G., Reisig, D. D., … Huseth, A. S. (2021). Landscape‐level variation in
            <i>Bt
            crops predict
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            ) resistance in cotton agroecosystems. Pest Management Science, 77(12), 5454–5462. 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, 261. 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, 14(6), 698–722. 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, 3(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

Winkel, M. A., Stallrich, J. W., Storlie, C. B., & Reich, B. J. (2021). Sequential Optimization in Locally Important Dimensions. TECHNOMETRICS, 63(2), 236–248. https://doi.org/10.1080/00401706.2020.1714738

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, 322, 107642. 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

Majumder, S., Guan, Y., Reich, B. J., O'Neill, S., & Rappold, A. G. (2021). Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 26(1), 23–44. https://doi.org/10.1007/s13253-020-00420-4

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

Hazra, A., Reich, B. J., & Staicu, A.-M. (2020). A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes. ENVIRONMETRICS, 31(3). https://doi.org/10.1002/env.2602

Guan, Q., Reich, B. J., Laber, E. B., & Bandyopadhyay, D. (2020). Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(531), 1066–1078. https://doi.org/10.1080/01621459.2019.1660169

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

Guan, Y., Johnson, M. C., Katzfuss, M., Mannshardt, E., Messier, K. P., Reich, B. J., & Song, J. J. (2020). Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(531), 1111–1124. https://doi.org/10.1080/01621459.2019.1665526

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, 69(4), 909–929. 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

Grantham, N. S., Guan, Y., Reich, B. J., Borer, E. T., & Gross, K. (2020). MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(530), 599–609. https://doi.org/10.1080/01621459.2019.1626242

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

Jhuang, A.-T., Fuentes, M., Bandyopadhyay, D., & Reich, B. J. (2020). Spatiotemporal signal detection using continuous shrinkage priors. STATISTICS IN MEDICINE, 39(13), 1817–1832. https://doi.org/10.1002/sim.8514

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, 109257. 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

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

Binion-Rock, S. M., Reich, B. J., & 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

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

Huang, Y.-N., Reich, B. J., Fuentes, M., & Sankarasubramanian, A. (2019). Complete spatial model calibration. The 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, June 1). 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

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

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. J. (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. J., & 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. J. (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. J., & 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. J., 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. J., 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. J., 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

Tsai, W.-L., Leung, Y.-F., McHale, M. R., Floyd, M. F., & Reich, B. J. (2018). 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

Kang, J., Reich, B. J., & 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., Guindani, M., Reich, B. J., 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

Morris, S. A., Reich, B. J., 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

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. J., Pacifici, K., & Lei, Y. (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. J., 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. J., 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. J., 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. J., & 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

Storlie, C. B., Reich, B. J., 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

Li, D., Reich, B. J., & 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).

Parker, R. J., Reich, B. J., & 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

Shaby, B. A., Reich, B. J., 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. J. (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

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. J., & 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. J., 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. J. (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), 31625. https://doi.org/10.1038/SREP31625

Parker, R. J., Reich, B. J., & 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

Schnell, P., Bandyopadhyay, D., Reich, B. J., & 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

Chang, H. H., Warren, J. L., Darrow, L. A., Reich, B. J., & 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, 16(3), 509–521. https://doi.org/10.1093/biostatistics/kxu060

Stephenson, A. G., Shaby, B. A., Reich, B. J., & 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., Reich, B. J., 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. J., Pacifici, K., Laber, E. B., Menninger, H. L., Henley, J. B., … Dunn, R. R. (2015). Fungi Identify the Geographic Origin of Dust Samples. PLOS ONE, 10(4), e0122605. https://doi.org/10.1371/journal.pone.0122605

Kao, Y., 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. J., Whetten, R. W., & McKeand, S. E. (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. J., 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. J. (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. J., & 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. J. (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. J., & Fuentes, M. (2015). Spatial Bayesian Nonparametric Methods. NONPARAMETRIC BAYESIAN INFERENCE IN BIOSTATISTICS, pp. 347–357. https://doi.org/10.1007/978-3-319-19518-6_17

Vock, L. F. B., Reich, B. J., 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. J., 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. J., 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

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

Wilson, A., & Reich, B. J. (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. J., 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. J. (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. J. (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. J., 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. J., 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. J., 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. J., & 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. J., & 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. J., & Porter, M. D. (2013, December). DISCUSSION OF "ESTIMATING THE HISTORICAL AND FUTURE PROBABILITIES OF LARGE TERRORIST EVENTS" BY AARON CLAUSET AND RYAN WOODARD. ANNALS OF APPLIED STATISTICS, Vol. 7, pp. 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. J., & 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. J., & 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. J. (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. J., & Miranda, M. L. (2012, January 15). Chang et al. Respond to "Environmental Exposures and Preterm Birth". AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol. 175, pp. 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. J. (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. J., & 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.

Reich, B. J., & 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

Bandyopadhyay, D., Reich, B. J., & 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. J., 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

Pati, D., Reich, B. J., & 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. J., & Haran, M. (2011, December). Guest Editors' Introduction to the Special Issue on "Computer Models and Spatial Statistics for Environmental Science". JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, Vol. 16, pp. 451–452. https://doi.org/10.1007/s13253-011-0071-9

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

Bondell, H. D., Reich, B. J., & Wang, H. (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. J., 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. J., & Slate, E. H. (2009, December 10). Bayesian modeling of multivariate spatial binary data with applications to dental caries. STATISTICS IN MEDICINE, Vol. 28, pp. 3492–3508. https://doi.org/10.1002/sim.3647

Costalonga, M., Batas, L., & Reich, B. J. (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. J. (2009, March). Simultaneous Factor Selection and Collapsing Levels in ANOVA. BIOMETRICS, Vol. 65, pp. 169–177. https://doi.org/10.1111/j.1541-0420.2008.01061.x

Choi, J., Fuentes, M., & Reich, B. J. (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. J., 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. J., & 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. J., & 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

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

Bondell, H. D., & Reich, B. J. (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

Reich, B. J., & 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. J., 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