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A Bayesian mixture of lasso regressions with t-errors

Author:
Cozzini, Alberto, Jasra, Ajay, Montana, Giovanni, Persing, Adam
Source:
Computational statistics & data analysis 2014 v.77 pp. 84-97
ISSN:
0167-9473
Subject:
Bayesian theory, Markov chain, algorithms, models, statistical analysis
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.
Agid:
5999462