A Pólya-Gamma Sampler for a Generalized Logistic Regression

L Dalla Valle, Fabrizio Leisen, Luca Rossini, Weixuan Zhu*

*Corresponding author for this work

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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.
Original languageEnglish
Pages (from-to)2899-2916
Number of pages0
JournalJournal of Statistical Computation and Simulation
Volume91
Issue number14
Early online date10 Apr 2021
DOIs
Publication statusPublished - 22 Sept 2021

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