Selected Recent Publications

by Joe Cavanaugh

Riedle, B., Neath, A. A. and Cavanaugh, J. E. (2020). Reconceptualizing the p–value from a likelihood ratio test: a probabilistic pairwise comparison of models based on Kullback–Leibler discrepancy measures. Journal of Applied Statistics, 47, 2582–2609. (doi:10.1080/02664763.2020.1754360)

Peterson, R. A. and Cavanaugh, J. E. (2020). Ordered quantile normalization: a semiparametric transformation built for the cross–validation era. Journal of Applied Statistics, 47, 2312–2327. (doi:10.1080/02664763.2019.1630372)

Cavanaugh, J. E. and Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Computational Statistics, 11:e1460. (doi:10.1002/wics.1460)

Liao, J. G., Cavanaugh, J. E. and McMurry, T. L. (2018). Extending AIC to best subset regression. Computational Statistics, 33, 787–806. (doi:10.1007/s00180-018-0797-8)

Ranapurwala, S. I., Cavanaugh, J. E., Young, T., Wu, H., Peek–Asa, C. and Ramirez, M. R. (2019). Public health application of predictive modeling: an example from farm vehicle crashes. Injury Epidemiology, 6:31. (doi:10.1186/s40621-019-0208-9)

by Kung-Sik Chan

Jarjour, R., & Chan, K. S. (2020). Dynamic conditional angular correlation. Journal of Econometrics, 216(1), 137-150.

Wang, C., & Chan, K. S. (2018). Quasi-likelihood estimation of a censored autoregressive model with exogenous variables. Journal of the American Statistical Association, 113(523), 1135-1145.

Su, F., & Chan, K. S. (2017). Testing for threshold diffusion. Journal of Business & Economic Statistics, 35(2), 218-227.

Stenseth, N. C., Chan, K. S., Tong, H., Boonstra, R., Boutin, S., Krebs, C. J., ... & Hurrell, J. W. (1999). Common dynamic structure of Canada lynx populations within three climatic regions. Science, 285(5430), 1071-1073.

Chan, K. S. (1993). Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. Annals of Statistics, 21(1), 520-533.

by Joyee Ghosh

Xun Li, Joyee Ghosh, and Gabriele Villarini, (2022+)"A Comparison of Bayesian Multivariate Versus Univariate Normal Regression Models for Prediction", The American Statistician, To Appear.

Xun Li, Joyee Ghosh, and Gabriele Villarini, (2022+) "Bayesian Negative Binomial Regression Model With Unobserved Covariates for Predicting the Frequency of North Atlantic Tropical Storms", Journal of Applied Statistics, To Appear.

Joyee Ghosh (2019), "Cauchy and Other Shrinkage Priors for Logistic Regression in the Presence of Separation" , Wiley Interdisciplinary Reviews: Computational Statistics, 11(6), e1478.

Gabriele Villarini, Beda Luitel, Gabriel A. Vecchi, and Joyee Ghosh (2019) "Multi-model ensemble forecasting of North Atlantic tropical cyclone activity", Climate Dynamics, 53(12), 7461-7477.

Joyee Ghosh, Yingbo Li , and Robin Mitra (2018), "On the use of Cauchy prior distributions for Bayesian logistic regression", Bayesian Analysis, 13(2), 359-383.

by Jian Huang

Y Gao, J Huang, Y Jiao, J Liu, X Lu, Z Yang (2020). Generative learning with Euler particle transport. arXiv:2012.06094v1. . (submitted to Mathematical and Scientific Machine Learning 2021).

S Lv, H Lin, H Lian, J Huang (2018). Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space. The Annals of Statistics 46 (2), 781-813.

J Huang, Y Jiao, Y Liu, X Lu (2018). A constructive approach to L0 penalized regression. The Journal of Machine Learning Research 19 (1), 403-439.

C Yi, J Huang (2017). Semismooth Newton coordinate descent algorithm for elastic-net penalized Huber loss regression and quantile regression. Journal of Computational and Graphical Statistics 26 (3), 547-557.

S Ma, J Huang (2017). A concave pairwise fusion approach to subgroup analysis. Journal of the American Statistical Association 112 (517), 410-423.

by Michael P. Jones

Jones MP, Perry SS, Thorne PS (2015). Maximum pairwise pseudo-likelihood estimation of the covariance matrix from left-censored data. Journal of Agricultural, Biological and Environmental Statistics, 20(1):83-99.

Saha C, Jones MP (2016). Type I and Type II error rates in the last observation carried forward method under informative dropout. Journal of Applied Statistics, 43(2):336-350.

Jones MP (2018). Linear regression with left-censored covariates and outcome using a pseudo-likelihood approach. Environmetrics, 29(8), e2536, p.1-16.

Lou Y, Jones MP & Sun W. (2019). Assessing the ratio of means as a causal estimand in clinical endpoint bioequivalence studies in the presence of intercurrent events. Statistics in Medicine, 38, 5214-5235.

Wu H, Jones MP (2021). Proportional likelihood ratio mixed model for discrete longitudinal data. Statistics in Medicine (in press).

by Ambrose Lo

Lo, A., Tang, Q., Tang, Z., 2021. Universally marketable insurance under multivariate mixtures. ASTIN Bulletin: The Journal of the International Actuarial Association, 51 (1), 221-243.

Lo, A., 2019. Demystifying the integrated tail probability expectation formula. The American Statistician, 73 (4), 367-374.

Cheung, K.C., Chong W.F., Lo, A., 2019. Budget-constrained optimal reinsurance design under coherent risk measures. Scandinavian Actuarial Journal, 2019 (9), 729-751.

Lo, A., Tang, Z., 2019. Pareto-optimal reinsurance policies in the presence of individual risk constraints. Annals of Operations Research, 274 (1-2), 395-423.

Lo, A., 2017. A Neyman–Pearson perspective on optimal reinsurance with constraints. ASTIN Bulletin: The Journal of the International Actuarial Association, 47 (2), 467-499.

by Lan Luo

Luo, L. and Song, P.X.K. (2020). Renewable estimation and incremental inference in generalized linear models with streaming datasets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82, Part1, 69-97.

Luo, L., She, X.C., Cao, J.X., Zhang, Y.L., Li, Y.J., Song, P.X.K. (2019). Detection and prediction of ovulation time from body temperature measured by YONO earbud. IEEE Transactions on Biomedical Engineering, 67(2): 512-522.

Shen, R., Luo, L. and Jiang, H. (2017). Identification of gene pairs through penalized regression subject to constraints. BMC Bioinformatics, 18: 466.

by Elias Shiu

H.U. Gerber, E.S.W. Shiu and J. Yang (2021). An Actuarial Approach to Pricing Barrier Options. European Actuarial Journal.

E.S.W. Shiu and X. Xiong (2021). An Elementary Derivation of Hattendorff’s Theorem. European Actuarial Journal.

H.U. Gerber, E.S.W. Shiu and H. Yang (2019). A Constraint-free Approach to Optimal Reinsurance. Scandinavian Actuarial Journal, 67-92.

H.U. Gerber, E.S.W. Shiu and H. Yang (2015). Geometric Stopping of a Random Walk and Its Applications to Valuing Equity-linked Death Benefits. Insurance: Mathematics and Economics, 64, 313–325.

H.U. Gerber, E.S.W. Shiu and H. Yang (2013). Valuing Equity-Linked Death Benefits in Jump Diffusion Models. Insurance: Mathematics and Economics, 53, 615–623.

by N. D. Shyamalkumar

Hong Beng Lim and Shyamalkumar, N. D. (2020). A Semiparametric Method for Assessing Life Expectancy Evaluations, North American Actuarial Journal (available online; 35 pages).

Shyamalkumar, N. D. and Tao, Siyang (2020). On Tail Dependence Matrices: The Realization Problem for Parametric Families, Extremes, 23, 245-285.

Ahn, J. Y. and Shyamalkumar, N. D. (2014). Asymptotic Theory for the Empirical Haezendonck-Goovaerts Risk Measure, Insurance: Mathematics and Economics, 55, No. 1, 78-90.

Chakraborty, I. and Shyamalkumar N. D. (2014) Revenue and Efficiency Ranking in Large Multi-Unit and Bundle Auctions, Journal of Mathematical Economics, 50, 12-21.

Russo, R. P. and Shyamalkumar, N. D. (2007). Reading Policies for Joins: An Asymptotic Analysis, Annals of Applied Probability, 17, 230-264.

by Sanvesh Srivastava

Srivastava, S. and Xu, Y. (2021). Distributed Bayesian Inference for Linear Mixed-Effects Models. Journal of Computational and Graphical Statistics.

Srivastava, S., DePalma, G., and C. Liu (2019). Journal of Computational and Graphical Statistics 28 (2), 233-243.

Srivastava, S., Li, C., and Dunson, D.B. (2018). Scalable Bayes via Barycenter in Wasserstein Space. The Journal of Machine Learning Research, 19 (1), 312–346.

Li, C., Srivastava, S., and Dunson, D. B. (2017). PIE: simple, scalable and accurate posterior interval estimation. Biometrika 104 (3), 665–680.

Srivastava, S., Engelhardt, B. E., and Dunson, D. B. (2017). Expandable factor analysis. Biometrika 104 (3), 649–663.

by Aixin Tan

Jin, R. and Tan, A. (2021). Fast Markov chain Monte Carlo for high dimensional Bayesian regression models with shrinkage priors. Journal of Computational and Graphical Statistics.

Liu, R. and Tan, A. (2020). Towards Interpretable Automated Machine Learning for STEM Career Prediction. Journal of Educational Data Mining, 12(2), 19-32.

Roy, V., Tan, A. and Flegal, J. (2018). Estimating standard errors for importance sampling estimators with multiple Markov chains. Statistica Sinica, 28, 1079-1101.

Tan, A. and Huang, J. (2016). Bayesian inference for high-dimensional linear regression under mnet priors. The Canadian Journal of Statistics, 44, 180-197.

Tan, A., Doss, H. and Hobert, J.P. (2015). Honest importance sampling with multiple Markov chains. Journal of Computational and Graphical Statistics, 24, 792-826.

by Boxiang Wang

Hao, B., Wang, B., Wang, P., Zhang, J., Yang, J., Sun, W. (2021) Sparse tensor additive regression. Journal of Machine Learning Research, 22(64), 1-43..

Wang, B. and Zou, H. (2019) A multicategory kernel distance weighted discrimination method for multiclass classification. Technometrics, 61(3), 396-408.

Wang, B. and Zou, H. (2018) Another look at distance-weighted discrimination. Journal of the Royal Statistical Society, Series B, 80(1), 177-198.

Koerner, T., Zhang, Y., Nelson, P., Wang, B., Zou, H. (2017) Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: A P3 study. Hearing Research, 350, 58-67.

Wang, B. and Zou, H. (2016) Sparse distance weighted discrimination. Journal of Computational and Graphical Statistics, 25(3), 826-838.

by Dale Zimmerman

Zimmerman, D. L., Zimmerman, N. D., Zimmerman, J. T. (2020). March Madness "Anomalies": Are they real, and if so, can they be explained? The American Statistician.

Wang, S., Zimmerman, D. L., Breheny, P. (2020). Sparsity-regularized skewness estimation for the multivariate skew normal and multivariate skew t distributions. Journal of Multivariate Analysis, 179.

Zimmerman, D. L., Tang, J., Huang, R. (2019). Outline analyses of the called strike zone in Major League Baseball. Annals of Applied Statistics, 13, 2416-2451.

Zimmerman, D. L., Ver Hoef, J. M. (2017). The Torgegram for fluvial variography: Characterizing spatial dependence on stream networks. Journal of Computational and Graphical Statistics, 26, 253-264.

Chang, S.-C., Zimmerman, D. L. (2016). Skew-normal antedependence models for skewed longitudinal data. Biometrika, 103, 363-376.