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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 https://doi.org/10.1007/s10182-021-00431-7.
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 https://doi.org/10.1016/j.csda.2022.107566.
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)
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.
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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.
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Hongqian Wu, Jones MP. (2021). Proportional likelihood ratio mixed model for discrete longitudinal data. Statistics in Medicine 40:2272-2285.
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Levchak, Philip, Karen Heimer, Joseph B. Lang and Janet L. Lauritsen. (2022). “Race and Trends in Men’s Imprisonment in the United States.” British Journal of Criminology. 62(5): 1233-1251. DOI: 10.1093/bjc/azac049
Zhu, Q. and Lang, J.B. (2022). Test-Inversion Confidence Intervals for Estimands in Contingency Tables Subject to Equality Constraints. Computational Statistics and Data Analysis, 169 (2022): 107413. https://doi.org/10.1016/j.csda.2021.107413
Lauritsen, J., Heimer, K., Lang, J. B. (2018). The Enduring Significance of Racial and Ethnic Disparities in Male Violent Victimization: An Analysis of National Crime Survey Victimization Data, 1973-2010. DuBois Review, 69-87.
Lang, J. B. (2017). Mean-Minimum Exact Confidence Intervals. The American Statistician, 71(4), 354-368.
H.U. Gerber and E.S.W. Shiu (2021). Equivalence Principle and Jewell's Inequality, European Actuarial Journal, 11, 725-730.
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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.
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Andrew M. Thomas (2023). Central limit theorems and asymptotic independence for local U-statistics on diverging halfspaces. Bernoulli, 29(4), 3280-3306.
Andrew M. Thomas, Peter A. Crozier, Yuchen Xu, and David S. Matteson (2023). Feature detection and hypothesis testing for extremely noisy nanoparticle images using topological data analysis. Technometrics, link.
Andrew M. Thomas (2022). The VC-dimension of a class of multiples of the primes, and a connection to AdaBoost. Online Journal of Analytic Combinatorics, 17, link.
Andrew M. Thomas and Takashi Owada (2021). Functional strong laws of large numbers for Euler characteristic processes of extreme sample clouds. Extremes, 24(4), 699-724.
Andrew M. Thomas and Takashi Owada (2021). Functional limit theorems for the Euler characteristic process in the critical regime. Advances in Applied Probability, 53(1), 57-80.
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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. https://doi.org/10.1016/j.jbankfin.2022.106544.
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Tang, Q., Tong, Z., and Xun, L., 2022. Portfolio Risk Analysis of Excess of Loss Reinsurance. Insurance: Mathematics and Economics, 102, pp.91–110. https://doi.org/10.1016/j.insmatheco.2021.11.004.
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. https://doi.org/10.1016/j.ejor.2020.10.043.
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Wang, B. and Zou, H. (2019) A multicategory kernel distance weighted discrimination method for multiclass classification. Technometrics, 61(3), 396-408.
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Wikle, N.B., Zigler, C.M. (2024+) Causal health impacts of power plant emission controls under mod[1]eled and uncertain physical process interference. Accepted to Annals of Applied Statistics, arXiv.
Wikle, N.B., Hanks, E.M., Henneman, L.R.F., and Zigler, C.M. (2022) A mechanistic model of annual sulfate concentrations in the United States. Journal of the American Statistical Association. 117(539), 1082–1093. doi:10.1080/01621459.2022.2027774, link.
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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, https://doi.org/10.1016/j.spasta.2021.100536
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.