International Day Of Women in Statistics and Data Science

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.