International Day Of Women in Statistics and Data Science: Full Schedule

Registration opens August 15th at www.idwsds.org.

You will be able to validate your attendance (Check-In to sessions) using your personal schedule.

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17:00 UTC

S302 - Emerging Computational Methods in Statistical Inference: Multivariate Analysis, Bayesian Shrinkage, and Advanced Regression Modeling

Tuesday October 6, 2026 17:00 - 18:00 UTC

Zoom Room #1

This session presents novel computational and methodological advancements in modern statistical inference, focusing on complex data structures such as multivariate, longitudinal, and high-dimensional survival data. The presentations span both frequentist and Bayesian paradigms, featuring innovative strategies to overcome long-standing computational bottlenecks commonly encountered in such investigations. The session opens with a new framework for multivariate order-restricted statistical inference under a general grass ordering, applying a pairwise optimization criterion and introducing an efficient vector projection-based algorithm to estimate the order-restricted mean matrix. It then transitions into the Bayesian realm with two complementary presentations on longitudinal quantile regression. These back-to-back talks first present a robust partially collapsed Gibbs sampler to conquer convergence failures in high dimensions and then showcase a Bayesian adaptive LASSO framework that utilizes clustering-based shrinkage for simultaneous optimal quantile estimation and sparse variable selection. The session concludes by extending advanced continuous shrinkage priors to survival analysis, presenting a hybrid minorize-maximize algorithm for high-dimensional proportional hazards models using the horseshoe+ prior. Together, these talks highlight recent developments of highly efficient algorithms to extract reliable inferences from high-dimensional and complicated datasets.

Speakers

Mai Dao

Assistant Professor, Wichita State University

Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →

Yujie Jia

Wichita State University

Yujie Jia is affiliated with Wichita State University, where she is currently a Ph.D. student in the Mathematics, Statistics, and Physics Department. Her research interests include multivariate order-restricted statistical inference, hypothesis testing, maximum likelihood estimation... Read More →

Sining Zhang

Wichita State University

Sining Zhang is a Ph.D. candidate in Applied Statistics at the Department of Mathematics, Statistics, and Physics at Wichita State University under the supervision of Dr. Mai Dao. Her research focuses on Bayesian quantile regression for longitudinal data, with an emphasis on partially... Read More →

Sarah Ghazawneh

Wichita State University

Sarah Ghazawneh is a Ph.D. candidate in Applied Mathematics at the Department of Mathematics, Statistics, and Physics at Wichita State University under the supervision of Dr. Mai Dao. Her research focuses on Bayesian quantile regression for longitudinal data, with an emphasis on efficient... Read More →

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