Bayesian binary regression with exponential power link

L. Naranjo, J. Martín and C.J. Pérez

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 464-476

Abstract: A flexible Bayesian approach to a generalized linear model is proposed to describe the dependence of binary data on explanatory variables. The inverse of the exponential power cumulative distribution function is used as the link to the binary regression model. The exponential power family provides distributions with both lighter and heavier tails compared to the normal distribution, and includes the normal and an approximation to the logistic distribution as particular cases. The idea of using a data augmentation framework and a mixture representation of the exponential power distribution is exploited to derive efficient Gibbs sampling algorithms for both informative and noninformative settings. Some examples are given to illustrate the performance of the proposed approach when compared with other competing models.

Keywords: Bayesian methods; Binary data; Data augmentation; Exponential power distribution; Generalized linear models; Gibbs sampling (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:464-476

DOI: 10.1016/j.csda.2012.07.022

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