Publications

Published/Accepted

• 2023
  • Tan, Y., Shenoy, P., Sherwood, B., Gaddy, M., Shenoy, C. and Oehlert, M. Bayesian Network Models for PTSD Screening in Veterans. Accepted at Informs Journal on Computing.
  • Sherwood, B. and Price, B.S. (2023) On the Use of Minimum Penalties in Statistical Learning. Journal of Computational and Graphical Statistics, 1-21.
  • Maidman, A, Wang, L, Zhou, X-H, Sherwood, B. (2023) Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline. Statistics in Medicine. 2023; 42(16): 2729–2745.
  • Allenbrand, C. and Sherwood, B. (2023) Model selection uncertainty and stability in beta regression models: A study of bootstrap-based model averaging with an empirical application to clickstream data. Annals of Applied Statistics, 17(1), 680-710.
• 2022
  • Sherwood, B. and Li, S. (2022) Quantile regression feature selection and estimation with grouped variables using Huber approximation. Statistics and Computing, 32, 75.
  • Price, B.S., Allenbrand, C. and Sherwood, B. (2022) Detecting Clusters in Multivariate Regression. WIREs Computational Statistics.
  • Sherwood, B. and Maidman, A. (2022) Additive nonlinear quantile regression in ultra-high dimension. Journal of Machine Learning Research, 23, 1-47.
• 2021
  • Price, B.S., Molstad, A.J. and Sherwood, B. (2021) Estimating Multiple Precision Matrices with Cluster Fusion Regularization. Journal of Computational and Graphical Statistics, 1-30.
• 2020
  • Sherwood, B., Molstad, A.J. and Singha, S. (2020) Asymptotic properties of concave L1-norm group penalties. Statistics and Probability Letters, 157.
• 2019
  • Nothing!
• 2018
  • Wang, L., Zhou, Y., Song, R. and Sherwood, B. (2018) Quantile-optimal treatment regimes. Journal of the American Statistical Association, 113, 1243-1254.
  • Price, B. S. and Sherwood, B. (2018) A Cluster Elastic Net for Multivariate Regression. Journal of Machine Learning Research, 18, 1-39.
  • Hong, X., Sherwood, B., Ladd-Acosta, C., Peng, S., Ji, H., Hao, K., Burd, I., Bartell, T.R., Wang, G., Tsai, H.J., Liu, X., Ji, Y., Wahl, A., Caruso, D., Lee-Parritz, A., Zuckerman, B. and Wang, X. (2018) Genome-wide DNA methylation associations with spontaneous preterm birth in US blacks: findings in maternal and cord blood samples. Epigenetics, 13:2, 163-172.
• 2017
  • Zhou, W., Sherwood, B., Ji, W., Du, F., Bai, J. and Ji, H. (2017) Genome-wide prediction of DNase I hypersensitivity using gene expression. Nature Communications, 8, 1-17.
  • Hong, X., Hao, K., Ji, H., Peng, S., Sherwood, B., Di Narzo, A., Tsai, H-J., Liu, X., Burd, I., Wang, G., Ji, Y., Caruso, D., Mao, G., Bartell, T., Zhang, Z., Pearson, C., Heffner, L., Cerda, S., Beaty, T., Fallin, M., Lee-Parritz, A., Zuckerman, B., Weeks, D. and Wang, X. (2017) Genome-wide approach identified a novel gene-maternal pre-pregnancy BMI interaction on preterm birth. Nature Communications, 8, 1-10.
• 2016
  • Zhou, W., Sherwood, B. and Ji, H. (2016) Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods and Challenges. Human Heredity, 81, 88-105.
  • Sherwood, B. (2016) Variable selection for additive partial linear quantile regression with missing covariates. Journal of Multivariate Analysis, 152, 206-223.
  • Hong, X., Ladd-Acosta, C., Hao, K., Sherwood, B., Ji, H., Keet, C.A., Kumar, R., Caruso, D., Liu, X., Wang, G., Chen, Z., Ji, Y., Mao, G., Walker, S.O., Bartell, T.R., Ji, Z., Sun, Y., Tsai, H-J., Pongracic, J.A., Weeks, D.E. and Wang, X. (2016) Epigenome-wide association study links site-specific DNA methylation changes with cow’s milk allergy. The Journal of Allergy and Clinical Immunology, 138, 908-911.
  • Wang, L. and Sherwood, B. (2016) Discussion of “Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood” by Yunwen Yang, Huixia Judy Wang and Xuming He. International Statistical Review, 84, 356-359.
  • Sherwood, B., Zhou, A., Weintraub, S. and Wang, L. (2016) Using quantile regression to create baseline norms for neuropsychological tests. Alzheimer’s & Dementia: Diagnosis & Disease Monitoring, 2, 12-18. (web application)
  • Sherwood, B. and Wang, L. (2016). Partially linear additive quantile regression in ultra-high dimension. Annals of Statistics, 44 288-317.(supplementary material, implementation code)
• 2015
  • Nothing!
• 2014
  • Wang, L., Sherwood, B. and Li, R. (2014). Discussion of “Estimation and Accuracy after Model Selection” by Brad Efron. Journal of the American Statistical Association, 109, 1007-1010.
• 2013
  • Sherwood, B., Wang, L. and Zhou, A. (2013). Weighted quantile regression for analyzing health care cost data with missing covariates. Statistics in Medicine, 32, 4967-4979.

Submitted

• Penalized method for combining logistic regression and naive bayes.
• An approach to model selection uncertainty in time series models.