Selected Recent Publications

by Joe Cavanaugh

Peterson, R. A. and Cavanaugh, J. E. (2022). Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials. To appear in AStA Advances in Statistical Analysis; available online at

Burghardt, E., Sewell, D. and Cavanaugh, J. (2022). Agglomerative and divisive hierarchical Bayesian clustering. To appear in Computational Statistics and Data Analysis; available online at

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)

by Kung-Sik Chan

Zhang, F., & Chan, K. S. (2022). Random projection ensemble classification with high‐dimensional time series. To appear in Biometrics.

Meng, J., & Chan, K. S. (2022). Penalized quasi-likelihood estimation of generalized Pareto regression–consistent identification of risk factors for extreme losses. Insurance: Mathematics and Economics, 104, 60-75.

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.

by Joyee Ghosh

Xun Li, Joyee Ghosh, and Gabriele Villarini, (2023+)"A Comparison of Bayesian Multivariate Versus Univariate Normal Regression Models for Prediction", The American Statistician, To Appear.

Xun Li, Joyee Ghosh, and Gabriele Villarini, (2023+) "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 Michael P. Jones

Martinez A, Awad AM, Jones MP, Hornbuckle KC. (2022). Intracity occurrence and distribution of airborne PCB congeners in Chicago. Science of the Total Environment 812:151505.

Hill CM, Jones MP, Chi DL. (2022). Effects of Adult Medicaid Dental Benefits Elimination on Child Dental Care Use. Medical Care 60(8):579-587.

Hongqian Wu, Jones MP. (2021). Proportional likelihood ratio mixed model for discrete longitudinal data. Statistics in Medicine 40:2272-2285.

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.

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

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 Li, L. (2022). Online two-way estimation and inference via linear mixed-effects models. To appear in Statistics in Medicine.

Luo, L., Zhou, L. and Song, P.X.K. (2022). Real-time regression analysis of streaming clustered data with possible abnormal data batches. Journal of the American Statistical Association (Theory & Methods).

Luo, L. and Song, P.X.K. (2021). Multivariate online regression analysis with heterogeneous streaming data. The Canadian Journal of Statistics.

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.

by Elias Shiu

H.U. Gerber and E.S.W. Shiu (2021). Equivalence Principle and Jewell's Inequality, European Actuarial Journal, 11, 725-730.

H.U. Gerber, E.S.W. Shiu and J. Yang (2021). An Actuarial Approach to Pricing Barrier Options. European Actuarial, 11, 333-339. Journal.

E.S.W. Shiu and X. Xiong (2021). An Elementary Derivation of Hattendorff’s Theorem. European Actuarial Journal, 11, 319-323.

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.

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

Im, Y. and Tan, A. (2021) Bayesian subgroup analysis in regression using mixture models. Computational Statistics and Data Analysis, 162.

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.

by Josh Zhiwei Tong

Gómez, F., Tang, Q., and Tong, Z., 2022. The Gradient Allocation Principle based on the Higher Moment Risk Measure. Journal of Banking and Finance, 143, 106544.

Tang, Q., Tong, Z., and Xun, L., 2022. Insurance Risk Analysis of Financial Networks Vulnerable to a Shock. European Journal of Operational Research, 301(2), pp.756–771.

Tang, Q., Tong, Z., and Xun, L., 2022. Portfolio Risk Analysis of Excess of Loss Reinsurance. Insurance: Mathematics and Economics, 102, pp.91–110.

Tang, Q., Tong, Z., and Yang, Y., 2021. Large Portfolio Losses in a Turbulent Market. European Journal of Operational Research, 292(2), pp.755–769.

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. and Ver Hoef, J.M. (2022). On deconfounding spatial confounding in linear models. The American Statistician, 76, 159–167.

Song, R. and Zimmerman, D.L. (2021). Modelling spatial correlation that grows on trees, with a stream network application. Spatial Statistics,

Karl, A.T. and Zimmerman, D.L. (2021). A diagnostic for bias in linear mixed model estimators induced by dependence between the random effects and the corresponding model matrix. Journal of Statistical Planning and Inference, 211, 107–118.

Zimmerman, D.L. and Lim, H.B. (2021). The middle-seed anomaly: Why does it occur in some sports tournaments but not others? Journal of Quantitative Analysis in Sports, 17, 171–185.

Zimmerman, D.L., Zimmerman, N.D., and Zimmerman, J.T. (2021). March Madness “Anomalies”: Are they real, and if so, can they be explained? The American Statistician, 75, 207–216.