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This work introduces a novel methodology based on finite mixtures ofStudent-t distributions to model the errors' distribution in linear regressionmodels. The novelty lies on a particular hierarchical structure for the mixturedistribution in which the first level models the number of modes, responsibleto accommodate multimodality and skewness features, and the second level modelstail behavior. Moreover, the latter is specified in a way that no degrees offreedom parameters are estimated and, therefore, the known statisticaldifficulties when dealing with those parameters is mitigated, and yet modelflexibility is not compromised. Inference is performed via Markov chain MonteCarlo and simulation studies are conducted to evaluate the performance of theproposed methodology. The analysis of two real data sets are also presented.

Bayesian linear regression models with flexible error distributions