A Bayesian Regression Model for the Non-standardized t Distribution with Location, Scale and Degrees of Freedom Parameters

In this paper we propose Bayesian non-standardized t regression models with unknown degrees of freedom, where both location and scale parameters follow regression structures, and a Bayesian method to fit the proposed models and obtain posterior parameter inferences when the degrees of freedom are as...

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Bibliographic Details
Published in:Sankhyā. Series B (2008) 2022-11, Vol.84 (2), p.809-830
Main Authors: Marín, Margarita, Cepeda-Cuervo, Edilberto
Format: Article
Language:English
Subjects:
Mathematics and Statistics
Statistics
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Summary:In this paper we propose Bayesian non-standardized t regression models with unknown degrees of freedom, where both location and scale parameters follow regression structures, and a Bayesian method to fit the proposed models and obtain posterior parameter inferences when the degrees of freedom are assumed to be continuous or discrete. Assuming uniform (discrete and continuous), exponential, Jeffreys and Poisson prior distributions, we develop a R-Bayesian t -regression package to obtain the posterior parameter estimates, applying a discrete and a continuous random walks in discrete and bounded real intervals, respectively. We also compare our proposal according to the usual maximum likelihood criteria, through simulations and an application to a financial dataset. In these simulations and the application, we find that our proposal has the best performance when we use the AIC, BIC and DIC criterions.
ISSN:0976-8386
0976-8394
DOI:10.1007/s13571-022-00288-z