A Bayesian mixture of lasso regressions with t-errors

TY - JOUR

T1 - A Bayesian mixture of lasso regressions with t-errors

AU - Cozzini, Alberto

AU - Jasra, Ajay

AU - Montana, Giovanni

AU - Persing, Adam

PY - 2014/9

Y1 - 2014/9

N2 - 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.

AB - 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.

KW - Mixture of regressions

KW - Variable selection

KW - Particle Markov chain Monte Carlo

KW - SEQUENTIAL MONTE-CARLO

KW - VARIABLE SELECTION

KW - FINITE MIXTURE

KW - MODELS

U2 - 10.1016/j.csda.2014.03.018

DO - 10.1016/j.csda.2014.03.018

M3 - Article

SN - 0167-9473

VL - 77

SP - 84

EP - 97

JO - COMPUTATIONAL STATISTICS AND DATA ANALYSIS

JF - COMPUTATIONAL STATISTICS AND DATA ANALYSIS

ER -