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.
DOI
10.1080/00949655.2021.1910947
Publication Date
2021-04-10
Publication Title
Journal of Statistical Computation and Simulation
ISSN
0094-9655
Embargo Period
2022-04-10
Organisational Unit
No Org Unit Found
Recommended Citation
Dalla, V. L., Leisen, F., Rossini, L., & Weixuan, Z. (2021) 'A Pólya-Gamma Sampler for a Generalized Logistic Regression', Journal of Statistical Computation and Simulation, . Available at: https://doi.org/10.1080/00949655.2021.1910947
