Bayesian modeling of location, scale, and shape parameters in skew‐normal regression models

In this paper, we propose Bayesian skew‐normal regression models where the location, scale and shape parameters follow (linear or nonlinear) regression structures, and the variable of interest follows the Azzalini skew‐normal distribution. A Bayesian method is developed to fit the proposed models, u...

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Bibliographic Details
Published in:Statistical analysis and data mining 2022-02, Vol.15 (1), p.98-111
Main Authors: Corrales, Martha Lucía, Cepeda‐Cuervo, Edilberto
Format: Article
Language:English
Subjects:
Bayesian analysis
Normal distribution
Parameters
Regression models
Skewed distributions
Statistical analysis
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Summary:In this paper, we propose Bayesian skew‐normal regression models where the location, scale and shape parameters follow (linear or nonlinear) regression structures, and the variable of interest follows the Azzalini skew‐normal distribution. A Bayesian method is developed to fit the proposed models, using working variables to build the kernel transition functions. To illustrate the performance of the proposed Bayesian method and application of the model to analyze statistical data, we present results of simulated studies and of the application to studies of forced displacement in Colombia.
ISSN:1932-1864
1932-1872
DOI:10.1002/sam.11548