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Activity Number: 355 - Advanced Bayesian Topics (Part 4)
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #319161
Title: Robust Bayesian Modeling Using Finite Mixtures of Student T Distributions
Companies: Federal University of Bahia
Keywords: finite mixtures; heavy tail distribution; linear regression; Bayesian inference

Statistical modeling based on finite mixtures of distributions is a research area in growing development and recently have been used to model the errors distribution in univariate and multivariate linear regression models. This work consider a methodology based on finite mixtures of Student-t distributions to model the errors distribution in linear regression models. The proposed approach contemplates a finite mixture in two levels considering in its specification separate structures for multimodality/skewness and tail behavior modeling, estimating the tail structure of the model without estimating degree of freedom parameters. The inference is performed via Markov chain Monte Carlo and simulation studies are conducted for to evaluate the performance of the proposed approach. Results from the analysis of a real data set are too presented.

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