A Bayesian mixture of lasso regressions with t-errors

Alberto Cozzini, Ajay Jasra, Giovanni Montana () and Adam Persing

Computational Statistics & Data Analysis, 2014, vol. 77, issue C, 84-97

Abstract: The following article considers a mixture of regressions with variable selection problem. In many real-data scenarios, one is faced with data which possess outliers, skewness and, simultaneously, one would like to be able to construct clusters with specific predictors that are fairly sparse. A Bayesian mixture of lasso regressions with t-errors to reflect these specific demands is developed. The resulting model is necessarily complex and to fit the model to real data, a state-of-the-art Particle Markov chain Monte Carlo (PMCMC) algorithm based upon sequential Monte Carlo (SMC) methods is developed. The model and algorithm are investigated on both simulated and real data.

Keywords: Mixture of regressions; Variable selection; Particle Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:77:y:2014:i:c:p:84-97

DOI: 10.1016/j.csda.2014.03.018

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