10/07/2022: 2:30 PM - 3:00 PM CDT
Concurrent
Room: Grand Ballroom Salon F
In this talk, we develop a Bayesian hierarchical model and associated computation strategy for simultaneously conducting parameter estimation and variable selection in binary quantile regression. We specify customary asymmetric Laplace distribution on the error term and assign quantile-dependent priors on the regression coefficients and a binary vector to identify model configuration.. Thanks to the normal-exponential mixture representation of the asymmetric Laplace distribution, we proceed to develop a novel three-stage computational scheme starting with an expectation-maximization algorithm and then the Gibbs sampler followed by an importance re-weighting step to draw nearly independent Markov chain Monte Carlo samples from the full posterior distributions of the unknown parameters. Simulation studies are conducted to compare the performance of the proposed Bayesian method with that of several existing ones in the literature. Finally, real-data applications are provided for illustrative purposes.
Binary quantile regression
Variable selection
Gibbs sampler
Importance sampling
Presenting Author
Mai Dao, Wichita State University
First Author
Mai Dao, Wichita State University
CoAuthor(s)
Souparno Ghosh, University of Nebraska - Lincoln
Min Wang, University of Texas at San Antonio
Target Audience
Mid-Level
Tracks
Knowledge
Women in Statistics and Data Science 2022