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Department of Statistics

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Our department excels at collaborative research between faculty, students and other universities. 

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

  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, 1-8.
  4. Sherlock, P., DiStefano, C., and Habing, B. (2022). Effects of mixing weights and predictor distributions. Structural Equation Modeling 29, 70-85.
  5. Petitbon, A. and Hitchcock, D. (2022). What Kind of Music Do You Like? A Statistical Analysis of Music Genre Popularity Over Time.  Journal of Data Science 20, 168-187.
  6. Zhou, H. and Huang, X. (2022). Bayesian beta regression for bounded responses with unknown supports. Computational Statistics and Data Analysis 167, 107345.
  7. Wang, C. and Lin, X. (2022). Bayesian semiparametric regression analysis of multivariate panel count data. Stats 5, 477-493.
  8. 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.
  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. Shin, M. and Liu, J. (2021+). Neuronized priors for Bayesian sparse linear regression. Journal of the American Statistical Association, in press.
  7. Gregory, K., Mammen, E., and Wahl, M. (2021). Statistical inference in sparse high-dimensional additive models. Annals of Statistics, 49(3), 1514-1536.
  8. Yang, Z. and Ho, Y. (2021+) Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data. Biometrics, in press.
  9. 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.
  10. 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.
  11. Wang, D. and Tang, C. (2021). Testing against uniform stochastic ordering with paired observations. Bernoulli 27, 2556-2563.
  12. Tang, C., Wang, D., El Barmi, H., and Tebbs, J. (2021). Testing for positive quadrant dependence. American Statistician 75, 23-30.
  13. Wang, D., Mou, X., and Liu, Y. (2021+). Varying-coefficient regression analysis for pooled biomarker data. Biometrics, in press.
  14. Kim, C., Lin, X., and Nelson, K. (2021). Measuring rater bias in diagnostic tests with ordinal ratings. Statistics in Medicine 40, 4014-4033.
  15. Wang, L. and Wang, L. (2021). Regression analysis of arbitrarily censored survival data under the proportional odds model. Statistics in Medicine 40, 3724-3739.
  16. 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.
  17. Sun, L., Li, S., Wang, L., Song, X., and Sui, X. (2021+). Simultaneous variable selection in regression analysis of multivariate interval-censored data. Biometrics, in press.
  18. 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.
  19. 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.
  20. 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.
  21. Huang, X. and Zhang, H. (2021). Corrected score methods for estimating Bayesian networks with error-prone nodes. Statistics in Medicine 40, 2692-2712.
  22. Huang, X. and Zhang, H. (2021). Tests for Gaussian Bayesian networks via quadratic inference functions. Computational Statistics and Data Analysis: 107209.
  23. Kim, T., Lieberman, B., Luta, G., and Peña, E. (2021). Prediction regions for Poisson-based regression models. Wiley Interdisciplinary Reviews: Computational Statistics, e1568.
  24. 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.
  25. 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.
  26. Bilder, C., Tebbs, J., and McMahan, C. (2021). Informative array testing with multiplex assays. Statistics in Medicine 40, 3021-3034.
  27. Liu, Y., McMahan, C., Tebbs, J., Gallagher, C., and Bilder, C. (2021). Generalized additive regression for group testing data. Biostatistics 22, 873-889.
  28. 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.
  29. 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.
  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, doi: 10.3390/jcdd7020019.
  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.

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