Howard Bondell

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

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

Liu, C., Yang, Y., Bondell, H., & Martin, R. (2021). BAYESIAN INFERENCE IN HIGH-DIMENSIONAL LINEAR MODELS USING AN EMPIRICAL CORRELATION-ADAPTIVE PRIOR. STATISTICA SINICA, 31(4), 2051–2072. https://doi.org/10.5705/ss.202019.0133

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

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

Zhao, Y., & Bondell, H. (2020). Solution paths for the generalized lasso with applications to spatially varying coefficients regression. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 142. https://doi.org/10.1016/j.csda.2019.106821

Tian, Y., Bondell, H. D., & Wilson, A. (2019). Bayesian variable selection for logistic regression. STATISTICAL ANALYSIS AND DATA MINING, 12(5), 378–393. https://doi.org/10.1002/sam.11428

Su, L., & Bondell, H. D. (2019). Best linear estimation via minimization of relative mean squared error. STATISTICS AND COMPUTING, 29(1), 33–42. https://doi.org/10.1007/s11222-017-9792-0

Liu, Z., & Bondell, H. D. (2019). Binormal Precision-Recall Curves for Optimal Classification of Imbalanced Data. STATISTICS IN BIOSCIENCES, 11(1), 141–161. https://doi.org/10.1007/s12561-019-09231-9

Stevenson, K. T., Peterson, M. N., & Bondell, H. D. (2018). Developing a model of climate change behavior among adolescents. CLIMATIC CHANGE, 151(3-4), 589–603. https://doi.org/10.1007/s10584-018-2313-0

Kong, D., Bondell, H. D., & Wu, Y. (2018). FULLY EFFICIENT ROBUST ESTIMATION, OUTLIER DETECTION AND VARIABLE SELECTION VIA PENALIZED REGRESSION. STATISTICA SINICA, 28(2), 1031–1052. https://doi.org/10.5705/ss.202016.0441

Frew, K. N., Peterson, M. N., Sills, E., Moorman, C. E., Bondell, H., Fuller, J. C., & Howell, D. L. (2018). Market and Nonmarket Valuation of North Carolina's Tundra Swans among Hunters, Wildlife Watchers, and the Public. WILDLIFE SOCIETY BULLETIN, 42(3), 478–487. https://doi.org/10.1002/wsb.915

Zhang, Y., & Bondell, H. D. (2018). Variable Selection via Penalized Credible Regions with Dirichlet-Laplace Global-Local Shrinkage Priors. BAYESIAN ANALYSIS, 13(3), 823–844. https://doi.org/10.1214/17-ba1076

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

Peterson, M. N., Chesonis, T., Stevenson, K. T., & Bondell, H. D. (2017). Evaluating relationships between hunting and biodiversity knowledge among children. Wildlife Society Bulletin, 41(3), 530–536. https://doi.org/10.1002/wsb.792

Huque, M. H., Bondell, H. D., Carroll, R. J., & Ryan, L. M. (2016). Spatial Regression with Covariate Measurement Error: A Semiparametric Approach. BIOMETRICS, 72(3), 678–686. https://doi.org/10.1111/biom.12474

Stevenson, K. T., Peterson, M. N., & Bondell, H. D. (2016). The influence of personal beliefs, friends, and family in building climate change concern among adolescents. Environmental Education Research, 25(6), 832–845. https://doi.org/10.1080/13504622.2016.1177712

Neely, M. L., Bondell, H. D., & Tzeng, J.-Y. (2015). A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies. BIOMETRICS, 71(2), 529–537. https://doi.org/10.1111/biom.12259

Kong, D., Bondell, H. D., & Wu, Y. (2015). Domain selection for the varying coefficient model via local polynomial regression. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 83, 236–250. https://doi.org/10.1016/j.csda.2014.10.004

Li, M., Staicu, A.-M., & Bondell, H. D. (2015). Incorporating covariates in skewed functional data models. Biostatistics (Oxford, England), 16(3), 413–426. https://doi.org/10.1093/biostatistics/kxu055

Chitwood, M. C., Peterson, M. N., Bondell, H. D., Lashley, M. A., Brown, R. D., & Deperno, C. S. (2015). Perspectives of wildlife conservation professionals on intensive deer management. Wildlife Society Bulletin, 39(4), 751–756. https://doi.org/10.1002/WSB.607

Jiang, L., Bondell, H. D., & Wang, H. J. (2014). Interquantile shrinkage and variable selection in quantile regression. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 69, 208–219. https://doi.org/10.1016/j.csda.2013.08.006

Huque, M. H., Bondell, H. D., & Ryan, L. (2014). On the impact of covariate measurement error on spatial regression modelling. ENVIRONMETRICS, 25(8), 560–570. https://doi.org/10.1002/env.2305

Stevenson, K. T., Peterson, M. N., Bondell, H. D., Moore, S. E., & Carrier, S. J. (2014). Overcoming skepticism with education: interacting influences of worldview and climate change knowledge on perceived climate change risk among adolescents. Climatic Change, 126(3-4), 293–304. https://doi.org/10.1007/s10584-014-1228-7

Stevenson, K. T., Peterson, M. N., Carrier, S. J., Strnad, R. L., Bondell, H. D., Kirby-Hathaway, T., & Moore, S. E. (2014). Role of Significant Life Experiences in Building Environmental Knowledge and Behavior Among Middle School Students. The Journal of Environmental Education, 45(3), 163–177. https://doi.org/10.1080/00958964.2014.901935

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

Sharma, D. B., Bondell, H. D., & Zhang, H. H. (2013). Consistent Group Identification and Variable Selection in Regression With Correlated Predictors. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 22(2), 319–340. https://doi.org/10.1080/15533174.2012.707849

Bondell, H. D., & Stefanski, L. A. (2013). Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 108(502), 644–655. https://doi.org/10.1080/01621459.2013.779847

Stevenson, K. T., Peterson, M. N., Bondell, H. D., Mertig, A. G., & Moore, S. E. (2013). Environmental, institutional, and demographic predictors of environmental literacy among middle school children. PLoS One, 8(3).

Post, J. B., & Bondell, H. D. (2013). Factor Selection and Structural Identification in the Interaction ANOVA Model. BIOMETRICS, 69(1), 70–79. https://doi.org/10.1111/j.1541-0420.2012.01810.x

Jiang, L., Wang, H. J., & Bondell, H. D. (2013). Interquantile Shrinkage in Regression Models. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 22(4), 970–986. https://doi.org/10.1080/10618600.2012.707454

Lin, C.-Y., Bondell, H., Zhang, H. H., & Zou, H. (2013). Variable selection for non-parametric quantile regression via smoothing spline analysis of variance. Stat, 2(1), 255–268. https://doi.org/10.1002/STA4.33

Gunes, F., & Bondell, H. D. (2012). A Confidence Region Approach to Tuning for Variable Selection. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 21(2), 295–314. https://doi.org/10.1080/10618600.2012.679890

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

Dalrymple, C. J., Peterson, M. N., Cobb, D. T., Sills, E. O., Bondell, H. D., & Dalrymple, D. J. (2012). Estimating public willingness to fund nongame conservation through state tax initiatives. Wildlife Society Bulletin, 36(3), 483–491. https://doi.org/10.1002/wsb.164

Peterson, M. N., Thurmond, B., Mchale, M., Rodriguez, S., Bondell, H. D., & Cook, M. (2012). Predicting native plant landscaping preferences in urban areas. Sustainable Cities and Society, 5, 70–76. https://doi.org/10.1016/j.scs.2012.05.007

Rodriguez, S. L., Peterson, M. N., Cubbage, F. W., Sills, E. O., & Bondell, H. D. (2012). Private landowner interest in market-based incentive programs for endangered species habitat conservation. Wildlife Society Bulletin, 36(3), 469–476. https://doi.org/10.1002/wsb.159

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

Freire, M., Robertson, I., Bondell, H. D., Brown, J., Hash, J., Pease, A. P., & Lascelles, B. D. X. (2011). RADIOGRAPHIC EVALUATION OF FELINE APPENDICULAR DEGENERATIVE JOINT DISEASE VS. MACROSCOPIC APPEARANCE OF ARTICULAR CARTILAGE. VETERINARY RADIOLOGY & ULTRASOUND, 52(3), 239–247. https://doi.org/10.1111/j.1740-8261.2011.01803.x

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.

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

Koehler, M. L., Bondell, H. D., & Tzeng, J.-Y. (2010). Evaluating Haplotype Effects in Case-Control Studies via Penalized-Likelihood Approaches: Prospective or Retrospective Analysis? GENETIC EPIDEMIOLOGY, 34(8), 892–911. https://doi.org/10.1002/gepi.20545

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., Krishna, A., & Ghosh, S. K. (2010). Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models. BIOMETRICS, 66(4), 1069–1077. https://doi.org/10.1111/j.1541-0420.2010.01391.x

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

Tzeng, J.-Y., & Bondell, H. D. (2009). A comprehensive approach to haplotype-specific analysis by penalized likelihood. European Journal of Human Genetics, 18(1), 95–103. https://doi.org/10.1038/ejhg.2009.118

Krishna, A., Bondell, H. D., & Ghosh, S. K. (2009). Bayesian variable selection using an adaptive powered correlation prior. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 139(8), 2665–2674. https://doi.org/10.1016/j.jspi.2008.12.004

Bondell, H. D., & Li, L. (2009). Shrinkage inverse regression estimation for model-free variable selection. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 71, 287–299. https://doi.org/10.1111/j.1467-9868.2008.00686.x

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

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

Bondell, H. D. (2008). A characteristic function approach to the biased sampling model, with application to robust logistic regression. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 138(3), 742–755. https://doi.org/10.1016/j.jspi.2007.01.004

Bondell, H. D. (2008). On robust and efficient estimation of the center of symmetry. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 37(3), 318–327. https://doi.org/10.1080/03610920701653144

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

Bondell, H. D., Liu, A., & Schisterman, E. F. (2007). Statistical inference based on pooled data: A moment-based estimating equation approach. JOURNAL OF APPLIED STATISTICS, 34(2), 129–140. https://doi.org/10.1080/02664760600994844

Bondell, H. D. (2007). Testing goodness-of-fit in logistic case-control studies. BIOMETRIKA, 94(2), 487–495. https://doi.org/10.1093/biomet/asm033