Bayesian Lasso Regularized Quantile Regression and Its Applications

Author

Priscilla Kissi-Appiah, The University of Texas Rio Grande Valley

Date of Award

12-1-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Applied Statistics and Data Science

First Advisor

Zhuanzhuan Ma

Second Advisor

Hansapani Rodrigo

Third Advisor

Santanu Chakraborty

Abstract

Since the pioneering work of (Koenker and Bassett Jr 1978), quantile regression has been a popular regression technique that helps researchers investigate a whole distribution of the response variable. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. In this research, we study the lasso regularized quantile regression from a Bayesian perspective. We develop an efficient sampling algorithm to generate posterior samplings for making posterior inference by using a location-scale mixture representation of the asymmetric Laplace distribution. The finite-sample performance of the proposed algorithm is investigated through various simulation studies and two real-data examples.

Comments

Copyright 2024 Priscilla Kissi-Appiah.

Recommended Citation

Kissi-Appiah, Priscilla, "Bayesian Lasso Regularized Quantile Regression and Its Applications" (2024). Theses and Dissertations. 1663.
https://scholarworks.utrgv.edu/etd/1663