Speaker: Dr. Carlos Lamarche from the University of Kentucky
Date and Time: Friday, December 3, 2021, 11:15am-12:15pm via Zoom. Please contact Qingning Zhou to obtain the Zoom link.
Title: Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data
Abstract: The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The new method is easy to implement and simulation studies show that it has accurate small sample behavior in comparison with existing procedures. Finally, we illustrate the new approach using U.S. Census data to estimate a model that includes more than eighty thousand parameters.