A Pólya-Gamma Sampler for a Generalized Logistic Regression

ORCID

Abstract

In this paper, we introduce a novel Bayesian data augmentation approach for estimating the parameters of the generalized logistic regression model. We propose a Pólya–Gamma sampler algorithm that allows us to sample from the exact posterior distribution, rather than relying on approximations. A simulation study illustrates the flexibility and accuracy of the proposed approach to capture heavy and light tails in binary response data of different dimensions. The algorithm performance is tested on simulated data. Furthermore, the methodology is applied to two different real datasets, where we demonstrate that the Pólya–Gamma sampler provides more precise estimates than the empirical likelihood method, outperforming approximate approaches.

Publication Date

2021-09-22

Publication Title

Journal of Statistical Computation and Simulation

Volume

91

Issue

14

ISSN

0094-9655

First Page

2899

Last Page

2916

This document is currently not available here.

10.1080/00949655.2021.1910947" data-hide-no-mentions="true">

Share

COinS