Below lists recent papers (accepted/in press) authored by our faculty (shown in bold).

 

Expand all2025

1. Deshpande, S. K., Bai, R., Balocchi, C., Starling, J. E., and Weiss, J. (2025). VCBART: Bayesian trees for varying coefficients. Bayesian Analysis. In press. 
2. Wang, S.-H., Bai, R., and Huang, H. H. (2025). Two-step mixed-type multivariate Bayesian sparse variable selection with shrinkage priors. Electronic Journal of Statistics, 19(1): 397-457. 
3. Zgodic, A., Bai, R., Zhang, J., Wang, Y., Olejua, P., and McLain, A. C. (2025). Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm. Statistics and Computing, 35(4): 109. 
4. Zhao, Z., Li, Y., Luo, X., and Bai, R. (2025). A unified three-state model framework for analysis of treatment crossover in survival trials. Statistics in Biopharmaceutical Resesarch, 17(3): 390-403. 
5. Consagra, W., Ning, L., and Rathi, Y. (2025) A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI. Medical Image Analysis, 102, 103537. 
6. Ning, Y., Sun, R., Hitchcock, D., Comert, G., and Chen, Y. (2025). Bayesian modeling of traffic-related air pollutants: A case study of urban transportation and air quality dynamics in Columbia, South Carolina. Atmospheric Environment: X. In press.
7. Pittman, R. D., and Hitchcock, D. B. (2025). Identifying influential observations in concurrent functional regression with weighted bootstrap. Advances in Data Analysis and Classification. In press. 
8. Woolsey, N. W., and Huang, X. (2025) Nonparametric regression for a circular response with error-in-covariate. Electronic Journal of Statistics. In press. 
9. Yu, Z., and Huang, X. (2025) Regression analysis of elliptically symmetric directional data. Computational Statistics and Data Analysis 208, 108167. 
10. Chan, K. P. F., Ma, T.F., Fang, H., Tsui, W. K., Ho, J. C. M., Ip, M. S. M., and Ho, P. L. (2025). Changes in the incidence, viral coinfection pattern and outcomes of pneumococcal hospitalizations during and after the COVID-19 pandemic. Pneumonia, 17, 9. 
11. Francis, H., Ma, T.F., Werle, R., Zegler, C., Smith, D., Soldat, D., Marin-Spiotta, E., and Ruark, M. (2025). Nitrogen fertilizer equivalence of red clover when inter-seeded into corn. Agronomy Journal, 117, e70133. 
12. Kwok, W. C., Ma, T.F., Tsui, C. K., Ho, J. C. M., and Tam, T. C. C. (2025). Prospective randomized study on switching triple inhaler therapy in COPD from multiple inhaler devices to single inhaler device in Chinese population. Chronic Obstructive Pulmonary Diseases-Journal of the COPD Foundation, 12(1): 52-60. 
13. Mandujano Reyes, J. F., McGahan, I. P., Ma, T.F., Ballmann, A. E., Walsh, D.P., and Zhu, J. (2025). Non-stationary extensions of the diffusion-based Gaussian Matérn field for ecological applications. Journal of Agricultural, Biological and Environmental Statistics. In press. 
14. Mandujano Reyes, J. F., Oh, G., McGahan, I. P., Ma, T.F., Russell, R., Walsh, D. P., and Zhu, J. (2025). Learning complex spatial dynamics of ecological processes with machine learning-guided partial differential equations. Environmental Data Science, 4: e28, 1-23.
15. Mandujano Reyes, J. F., Ma, T. F., McGahan, I. P., Storm, D. J., Walsh, D. P., and Zhu, J. (2025). Spatiotemporal causal inference with mechanistic ecological models: evaluating targeted culling on chronic wasting disease dynamics in cervids. Environmetrics, 36, e2901. 
16. Wong, C. K., Ho, I., Choo, A., Lau, R., Ma, T.F., Chiu, A.C.H.O., Lam, T. H., Lin, M., Leung, R. W. H., and Foo, D. C. C. (2025). Cardiovascular safety of 5-fluorouracil and capecitabine in colorectal cancer patients: real-world evidence. Cardio-Oncology, 11,  3. 
17. Mao, Y., Wang, L., and Sui, X. (2025). Bayesian joint analysis of longitudinal data and interval-censored failure time data. Lifetime Data Analysis. In press.
18. Fang, L., Li, S., Hu, T., Wang, L., McMahan, C., and Tebbs, J. (2025). Probit time-to-event regression for misclassified group testing data. Statistica Sinica. In press. 
19. Li, S., Sun, L., Wang, L., and Tu, W. (2025). A general class of transformation cure rate frailty models for multivariate interval-censored data.  Communications in Mathematics and Statistics. In press.
20. McMahan, C., Joyner, C., Tebbs, J., and Bilder, C. (2025). A mixed effects Bayesian regression model for multivariate group testing data. Biometrics 81(1), ujaf028.
21. St. Ville, M., McMahan, C., Bible, J., Tebbs, J., and Bilder, C. (2025). Bayesian additive regression trees for group testing data. Statistics in Medicine 44(6), e70052. 

2024

1. Wang, S., Shin, M., and Bai, R. (2024). Generative quantile regression with variability penalty. Journal of Computational and Graphical Statistics, 33, 1202-1213. 
2. Wang, S., Shin, M., and Bai, R. (2024). Fast bootstrapping nonparametric maximum likelihood for latent mixture models. IEEE Signal Processing Letters 31, 870-874. 
3. Liu, Q., Huang, X., and Bai, R. (2024). Bayesian modal regression based on mixture distributions. Computational Statistics & Data Analysis, 199: 108012. 
4. Shen, Q., Gregory, K., and Huang, X. (2024). Post-selection inference in regression models for group testing data. Biometrics, 80(3), ujae101. 
5. Zhong, S., and Hitchcock, D.B. (2024). Functional clustering of fictional narratives using Vonnegut curves. Advances in Data Analysis and Classification, 18, 1045-1066. 
6. Hitchcock, D.B. (2024). Lessons from a discussion-based course on the history of statistics. The American Statistician, 78, 368-374. 
7. Shayan, A. M., Hitchcock, D. B., Singh, S., Gao, J., Groff, R. E., and Singapogu, R. B. (2024). Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment. IEEE Journal of Biomedical and Health Informatics, 29, 2981-2992. 
8. Zhang W., Ma Z., Ho Y., Yang S., Habiger J.D, Huang H.-H., and Huang Y. (2024). Multi-omics integrative analysis for incomplete data using weighted p-value adjustment approach. Journal of Agricultural, Biological, and Environmental Statistics. In press. 
9. Liu, Q., and Huang X. (2024). Parametric modal regression with error in covariates. Biometrical Journal 66, 2200348. 
10. Liu, Q., Huang X., and Zhou H. (2024). The flexible Gumbel distribution: A new model for inference about the mode. Stats, 7, 317-332. 
11. Yu, Z., and Huang X. (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics, 18, 301-334. 
12. Huang, X., and Zhang, H. (2024). Detecting responsible nodes in differential Bayesian networks. Statistics in Medicine. In press. 
13. Wang, L., Wang, C., Lin, X., and Wang, L. (2024). Bayesian regression analysis of panel count data under frailty nonhomogeneous Poisson process model with an unknown frailty distribution. Electronic Journal of Statistics, 18:3687-3705. 
14. Chan, F. K. P., Ma, T.F., Sridhar, S., Lui, M. M. S., Ho, J. C. M., Lam, D. C. L., Ip, M. S. M., and Ho, P. L. (2024). Changes in the incidence, clinical features and outcomes of tuberculosis during COVID-19 Pandemic. Journal of Infection and Public Health, 17(9), 102511. 
15. Daniels, M. C., Braziunas, K. H., Turner, M. G., Ma, T.F., Short, K. C., and Rissman, A. R. (2024) Multiple social and environmental factors affect wildland fire response of full or less-than-full suppression. Journal of Environmental Management, 351, 119731. 
16. Kwok, W.C., Lung, D.C., Tam, T.C.C., Yap, D.Y.H., Ma, T.F., Tsui, C.K., Zhang, R., Lam, D.C.L., Ip, M.S.M., and Ho, J.C.M. (2024). Protective effects from prior pneumococcal vaccination in patients with chronic airway diseases during hospitalization for influenza - A territory-wide study, Vaccines. 12(7), 704. 
17. Kwok, W.C., Ma, T.F., and Tam, T.C.C. (2024). Predicting the prognosis and survival in early-stage lung cancer after curative surgery. Journal of Oncology Research and Treatment, 8, 1000228. 
18. Ma, T.F., Cai, Y., Shi, P., and Zhu, J. (2024). Hierarchical dependence modeling for the analysis of large insurance claims data. The Annals of Applied Statistics, 18, 1402-1420. 
19. Ma, T.F., Mandujano Reyes, J.F., and Zhu, J. (2024). M-estimators for models with a mix of discrete and continuous parameters. Sankhya A, 86, 164-190. 
20. Mandujano Reyes, J. F., Ma, T.F., McGahan, I. P., Storm, D. J., Walsh, D. P., and Zhu, J. (2024). Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease. Spatial Statistics, 62, 100850. 
21. Mo, C., Ma, T.F., and McPherson, B. ABO blood group and cochlear function-evidence from a large sample size study. International Journal of Audiology, 63, 106-116. 
22. Zhao, Z., Ma, T.F., Ng, W.L., and Yau, C.Y. (2024). A composite likelihood-based approach for change-point detection in spatio-temporal processes. Journal of the American Statistical Association, 119(548), 3086-3100. 
23. Shin, M., Wang, S., and Liu, J.S. (2024). Generative multi-purpose sampler for weighted M-estimation. Journal of Computational and Graphical Statistics, 33(3), 1084-1097. 
24. Li, S., Hu, T., Wang, L., McMahan, C., and Tebbs, J. (2024). Regression analysis of group-tested current status data. Biometrika, 111:1047-1061. 
25. Mou, X., and Wang, D. (2024). Additive partially linear model for pooled biomonitoring data. Computational Statistics and Data Analysis 190, 107862. 
26. Weaver, D. (2024). The mortality experience of disabled persons in the United States during the COVID-19 pandemic. Health Affairs Scholar, 2, qxad082. 
27. Shi, B., Liu, Y., Xie, S., Zhu, X., Wang, Y. (2024). Network-Assisted Mediation Discovery with High-Dimensional Neuroimaging Mediators. Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 1-22. 
28. McDonnell, E., Xie, S., Marder, K., Cui, F., Wang, Y. (2024). Dynamic Undirected Graphical Models for Time-Varying Clinical Symptom and Neuroimaging Networks. Statistics in Medicine 43 (1), 4131-4147. 
29. Xie, S., Zeng, D., Wang, Y. (2024). Identifying Temporal Pathways Using Biomarkers in the Presence of Latent Non-Gaussian Components. Biometrics 80 (2), ujae033. 
30. Xie, S., Ogden, RT. (2024). Functional Support Vector Machine. Biostatistics 25 (4), 1178-1194. 

2023

1. Allotey, P., and Harel, O. (2023). Modeling geostatistical incomplete spatial correlated survival data with applications to COVID-19 mortality in Ghana. Spatial Statistics, 54, 100730. 
2. Allotey, P., and Harel, O. (2023). Bayesian spatial modeling of incomplete data with application to HIV prevalence in Ghana. Sankhya B, 85, 307-329. 
3. Bai, R., Boland, M. R., and Chen, Y. (2023). Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance. Journal of Machine Learning Research, 24, 1-49. 
4. Mitchell, E., Dryden, I., Fallaize, C.J., Andersen, R., Bradley, A.V., Large, D.J., and Sowter, A. (2023). Model code for object oriented data analysis of surface motion time series in peatland landscapes. NERC EDS Environmental Information Data Centre. 
5. Dryden, I. (2023). Comments on: Shape-based functional data analysis. TEST. 
6. Sun, N., Bursac, Z., Dryden, I., Lucchini, R., Dabo‑Niang, S., and Ibrahimou, B. (2023). Bayesian spatiotemporal modelling for disease mapping: an application to preeclampsia and gestational diabetes in Florida, United States. Environmental Science and Pollution Research, 30, 109283-109298. 
7. Liu, Z., Hitchcock, D.B., and Singapogu, R. (2023). Cannulation skill assessment using functional data analysis. IEEE Journal of Biomedical and Health Informatics, 27, 4512-4523. 
8. Zhang, H., Huang, X, and Arshad, H. (2023) Comparing dependent undirected Gaussian networks. Bayesian Analysis, 18, 1341-1366. 
9. Chan, K.P.F., Ma, T.F., Sridhar, S., Lam, D.C.L., Ip, M.S.M., and Ho, P.L. (2023). Changes in etiology and clinical outcomes of pleural empyema during the COVID-19 pandemic. Microorganisms, 11, 303.
10. Kwok, W.C., Ho, J.C.M., Ma, T.F., Lam, D.C.L., Chan, J.W.M., Ip, M.S.M, and Tam, T.C.C. (2023). Risk of hospitalized bronchiectasis exacerbation based on blood eosinophil counts among Chinese patients. The International Journal of Tuberculosis and Lung Disease, 27, 61-65. 
11. Kwok, W.C., Ma, T.F., Ho, J., Lam, D. C. L., Sit, K. Y., Ip, M.S.M., Au, T.W.K., and Tam, T.C.C. (2023). Prediction model on disease recurrence for low-risk resected stage I lung adenocarcinoma. Respirology, 28, 669-676.
12. Ma, T.F., Wang, F., and Zhu, J. (2023). On generalized latent factor modeling and inference for high-dimensional binomial data. Biometrics, 79, 2311-2320. 
13. Ma, T.F., Wang, F., Zhu, J., Ives, R.I., and Lewińska, K.E. (2023). Scalable semiparametric spatio-temporal regression for large data analysis. Journal of Agricultural, Biological and Environmental Statistics, 28, 279-298. 
14. Mo, C., McPherson, B., and Ma, T.F. (2023). Cochlear function in individuals with and without spontaneous otoacoustic emissions. Audiology Research, 13, 686-699.
15. Oh, G., Aravamuthan, S., Ma, T.F., McGahan, I., Zhu, J., Ballmann, A., Russell, R., and Walsh, D. (2023). Model-based surveillance system design under practical constraints with application to white nose syndrome. Environmental and Ecological Statistics, 30, 649-667.
16. Qiang, B. and Pena, E. (2023). Robust simultaneous estimation of location parameters. Statistics and Probability Letters, 193, 109730.
17. Gel, Y., Pena, E. and Wang, H. (2023). Conversations with Gabor Szekely. Statistical Science, 38, 355-367. 
18. Warasi, M., Tebbs, J., McMahan, C., and Bilder, C. (2023). Estimating the prevalence of two or more diseases using outcomes from multiplex group testing. Biometrical Journal, 65, 2200270. 
19. Bilder, C., Hitt, B., Biggerstaff, B., Tebbs, J., and McMahan (2023). bigGroup2: An R package for group testing. R Journal, 15, 21-36. 
20. Wang, D., Mou, X., and Liu, Y. (2023). Varying-coefficient regression analysis for pooled biomarker data. Biometrics, 78, 1328-1341.
21. Withana Gamage, P., McMahan, C., and Wang, L. (2023). A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model. Lifetime Data Analysis, 29, 188-212. 
22. Zhang, W., Ma, Z., Wang, L., Fan, D., and Ho Y. (2023). Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection. Statistics in Medicine 42, 5616-5629. 
23. Chakrabarti, M., Jiao, K., Potts, J.D., Wang, L., Branch, S., Harrelson, S., Khan, S., and Mohamad Azhar (2023). Hippo signaling mediates TGFß-dependent transcriptional inputs in cardiac cushion mesenchymal cells to regulate extracellular matrix remodeling. Journal Cardiovascular Development and Disease, 10, 483. 

2022

1. Bai, R., Moran, G., Antonelli, J., Chen, Y., and Boland, M. (2022). Spike-and-slab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association, 117, 184-197. 
2. Meeker, J., Burris, H., Bai, R., Levine, L., and Boland, M. (2022). Neighborhood deprivation increases the risk of post-induction cesarean delivery. Journal of the American Medical Informatics Association, 29, 329-334. 
3. Cao, X., Gregory, K., and Wang, D. (2022). Inference for sparse linear regression based on the leave-one-covariate-out solution path. Communications in Statistics: Theory and Methods, 52, 6640-6657. 
4. Sherlock, P., DiStefano, C., and Habing, B. (2022). Effects of mixing weights and predictor distributions. Structural Equation Modeling, 29, 70-85. 
5. Mo, Y., Habing, B., and Sedransk, N. (2022). Tree-based methods: A tool for modeling nonlinear complex relationships and generating new insights from data. Journal of Data Science, 3, 359-379. 
6. Petitbon, A., and Hitchcock, D.B. (2022). What Kind of Music Do You Like? A Statistical Analysis of Music Genre Popularity Over Time.  Journal of Data Science, 20, 168-187. 
7. Phillips, R. C., Samadi, S., Hitchcock, D. B., Meadows, M. E., and Wilson, C. A. M. E. (2022). The devil is in the tail dependence: An assessment of multivariate copula-based frameworks and dependence concepts for coastal compound flood dynamics. Earth’s Future, 10, e2022EF002705.
8. Yang, Z., and Ho, Y.Y. (2022). Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data. Biometrics, 78, 766-776.
9. Zhou, H. and Huang, X. (2022). Bayesian beta regression for bounded responses with unknown supports. Computational Statistics and Data Analysis, 167, 107345.
10. Wang, C. and Lin, X. (2022). Bayesian semiparametric regression analysis of multivariate panel count data. Stats, 5, 477-493. 
11. Foreman-Ortiz, I. U., Ma, T.F., Hoover, B. M., Wu, M., Murphy, C. J., Murphy, R. M., and Pedersen, J. A. (2022) Nanoparticle tracking analysis and statistical mixture distribution analysis to quantify nanoparticle-vesicle binding. Journal of Colloid and Interface Science, 615, 50-58. 
12. Kwok, W. C., Cheung, K. S., Ho, J. C. M., Li, B., Ma, T.F., and Leung, W. K. (2022). High-dose proton pump inhibitors are associated with hospitalization for bronchiectasis exacerbation. The International Journal of Tuberculosis and Lung Disease 26, 917-921.
13. Kwok, W.C., Ma, T.F., Chan, J.W.M., Pang, H.H., and Ho, J.C.M. (2022) A multicenter retrospective cohort study on predicting the risk for amiodarone pulmonary toxicity. BMC Pulmonary Medicine, 22, 128.
14. Shin, M. and Liu, J. (2022). Neuronized priors for Bayesian sparse linear regression. Journal of the American Statistical Association 17, 16Ho,95-1710. 
15. Park, J., Jeon, Y., Shin, M., Jeon, M., and Jin, I. (2022). Bayesian shrinkage for functional network models with intractable normalizing constants. Journal of Computational and Graphical Statistics, 31, 360-377. 
16. Wang, D., Mou, X., and Liu, Y. (2022). Varying coefficient regression analysis for pooled biomonitoring data. Biometrics, 78, 1328-1341. 
17. Sun, L., Li, S., Wang, L., Song, X., and Sui, M. (2022). Simultaneous variable selection for joint models of multivariate interval-censored data. Biometrics, 78, 1402-1413. 

2021

1. Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in high-dimensional Bayesian regression models. Statistica Sinica 31, 843-865.
2. Bai, R., Rockova, V., and George, E. (2021). Spike-and-slab meets LASSO: A review of the spike- and-slab LASSO. In Tadesse, M. and Vannucci, M. (Eds.), Handbook of Bayesian Variable Selection (pp 81-108). Chapman & Hall/CRC Press.
3. Meeker, J., Canelon, S., Bai, R., Levine, L., and Boland, M. (2021). Individual- and neighborhood- level risk factors for severe maternal morbidity. Obstetrics & Gynecology 137, 847-854.
4. Boland, M., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mowery, D., and Herman, D. (2021). A method to link neighborhood-level covariates to COVID-19 infection patterns in Philadelphia using spatial regression. AMIA Annual Symposium Proceedings 2021, 545-554.
5. Shin, M., Cho, H., Min, H., and Lim, S. (2021). Neural bootstrapper. Advances in Neural Information Processing Systems 34, NeurIPS 2021 Proceedings.
6. Gregory, K., Mammen, E., and Wahl, M. (2021). Statistical inference in sparse high-dimensional additive models. Annals of Statistics, 49(3), 1514-1536.
7. Peterson, L., Oram, M., Flavin, M., Seabloom, D., Smith, W., O’Sullivan, M., Vevang, K., Upadhyaya, P., Stornetta, A., Floeder, A., Ho, Y., and others (2021). Co-exposure to inhaled aldehydes or carbon dioxide enhances the carcinogenic properties of the tobacco specific nitrosamine 4- methylanitrosamino-1-(3-pyridyl)-1-butanone (NNK) in the A/J mouse lung. Chemical Research in Toxicology 34, 723-732.
8. Lieberman, B., Kusi, M., Hung, C., Chou, C., He, N., Ho, Y., and others (2021). Toward uncharted territory of cellular heterogeneity: Advances and applications of single-cell RNA-seq. Journal of Translational Genetics and Genomics 5, 1-21.
9. Wang, D. and Tang, C. (2021). Testing against uniform stochastic ordering with paired observations. Bernoulli 27, 2556-2563.
10. Tang, C., Wang, D., El Barmi, H., and Tebbs, J. (2021). Testing for positive quadrant dependence. American Statistician 75, 23-30.
11. Kim, C., Lin, X., and Nelson, K. (2021). Measuring rater bias in diagnostic tests with ordinal ratings. Statistics in Medicine 40, 4014-4033.
12. Wang, L. and Wang, L. (2021). Regression analysis of arbitrarily censored survival data under the proportional odds model. Statistics in Medicine 40, 3724-3739.
13. Sun, L., Li, S., Wang, L., and Song, X. (2021). A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup. Statistical Methods in Medical Research 30, 1890-1903.
14. Pittman, R., Hitchcock, D., and Grego, J. (2021). Concurrent functional regression to reconstruct river stage data during flood events. Environmental and Ecological Statistics 28, 219-237.
15. Zhong, S. and Hitchcock, D. (2021). S&P 500 stock price prediction using technical, fundamental and text data. Statistics, Optimization & Information Computing 9, 769-788.
16. Zhang, H., Huang, X., Han, S., Rezwan, F., Karmaus, W., Arshad, H., and Holloway, J. (2021). Gaussian Bayesian network comparisons with graph ordering unknown. Computational Statistics and Data Analysis: 107156.
17. Huang, X. and Zhang, H. (2021). Corrected score methods for estimating Bayesian networks with error-prone nodes. Statistics in Medicine 40, 2692-2712.
18. Huang, X. and Zhang, H. (2021). Tests for Gaussian Bayesian networks via quadratic inference functions. Computational Statistics and Data Analysis: 107209.
19. Kim, T., Lieberman, B., Luta, G., and Peña, E. (2021). Prediction regions for Poisson-based regression models. Wiley Interdisciplinary Reviews: Computational Statistics, e1568.
20. Kim, T., Lieberman, B., Luta, G., and Peña, E. (2021+). Prediction regions for Poisson and over- dispersed Poisson regression models with applications in forecasting number of deaths during the covid-19 pandemic. Open Statistics 2, 81-112.
21. Watson, S., Cooper, P., Liu, N., Gharraee, L., Du, L., Han, E., Peña, E., and others (2021). Diet alters age-related remodeling of aortic collagen in mice susceptible to atherosclerosis. American Journal of Physiology 320: H52-H65.
22. Bilder, C., Tebbs, J., and McMahan, C. (2021). Informative array testing with multiplex assays. Statistics in Medicine 40, 3021-3034.
23. Liu, Y., McMahan, C., Tebbs, J., Gallagher, C., and Bilder, C. (2021). Generalized additive regression for group testing data. Biostatistics 22, 873-889.
24. Bilder, C., Tebbs, J., and McMahan, C. (2021). Discussion on “Is group testing ready for prime-time in disease identification?” Statistics in Medicine 40, 3881-3886.
25. Mokalled, S., McMahan, C., Tebbs, J., Brown, D., and Bilder, C. (2021). Incorporating the dilution effect in group testing regression. Statistics in Medicine 40, 2540-2555.

2020

1. Joyner, C., McMahan, C., Tebbs, J., and Bilder, C. (2020). From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data. Biometrics 76, 913-923.
2. Hou, P., Tebbs, J., Wang, D., McMahan, C., and Bilder, C. (2020). Array testing with multiplex assays. Biostatistics 21, 417-431.
3. Wang, D., Tang, C., and Tebbs, J. (2020). More powerful goodness-of-fit tests for uniform stochastic ordering. Computational Statistics and Data Analysis 144, 106898.
4. Bilder, C., Iwen, P., Abdalhamid, B., Tebbs, J., and McMahan, C. (2020). Tests in short supply? Try group testing. Significance 17, 15-16.
5. Chakrabarti, M., Al-Sammarraie, N., Gebere, M., Bhattacharya, S., Johnson, J., Peña, E., and others (2020). Transforming growth factor Beta3 is required for cardiovascular development. Journal of Cardiovascular Development and Disease 7, 19.
6. Huang, X. and Zhou, H. (2020). Conditional density estimation with covariate measurement error. Electronic Journal of Statistics 14, 970-1023.
7. Zhou, H. and Huang, X. (2020). Parametric mode regression for bounded responses. Biometrical Journal 61, 1791-1809.
8. Wang, D., Mou, X., Li, X., and Huang, X. (2020). Local polynomial regression for pooled response data. Journal of Nonparametric Statistics 32, 814-837.
9. Liu, H., Hitchcock, D., Samadi, S. (2020). Spatio-temporal analysis of flood data from South Carolina. Journal of Statistical Distributions and Applications 7, 11. 
10. Samadi, S., Pourreza‐Bilondi, M., Wilson, C., and Hitchcock, D. (2020). Bayesian model averaging with fixed and flexible priors: Theory, concepts, and calibration experiments for rainfall‐runoff modeling. Journal of Advances in Modeling Earth Systems, 12, e2019MS001924.
11. Liu, Q., Hodge, J., Wang, J., Wang, Y., Wang, L., and others (2020). Emodin reduces breast cancer lung metastasis by suppressing macrophage-induced breast cancer cell epithelial mesenchymal transition and cancer stem cell formation. Theranostics 10, 8365-8381.
12. Mohammadi, E., Gregory, K., Thelwall, M., and Barahmand, N. (2020). Which health and biomedical topics generate the most Facebook interest and the strongest citation relationships? Information Processing and Management 57, 102230.
13. Baek S., Ho, Y., and Ma, Y. (2020). Using sufficient direction factor model to analyze latent activities associated with breast cancer survival. Biometrics 76, 1340-1350. 
14. Ma Z., Hanson T., Ho, Y. (2020). Flexible bivariate count data regressions. Statistics in Medicine 39, 3476-3490.
15. Krizek, B., Blakley, I., Freese, N., Ho, Y., and Loraine A. (2020). The Arabidopsis transcription factor AINTEGUMENTA orchestrates patterning genes and auxin signaling in the establishment of flora growth and form. Plant Journal 103, 752-768.
16. Yun, J., Shin, M., Jin, I., and Liang, F. (2020). Stochastic approximation Hamiltonian Monte Carlo. Journal of Statistical Computation and Simulation 90, 3135-3156.
17. Shin, M., Bhattacharya, A., and Johnson, V. (2020). Functional horseshoe prior for subspace shrinkage. Journal of the American Statistical Association 115, 1784-1797.