A Bayesian mixture of lasso regressions with t-errors

作者：Cozzini Alberto; Jasra Ajay^*; Montana Giovanni; Persing Adam

来源：Computational Statistics & Data Analysis, 2014, 77: 84-97.

DOI：10.1016/j.csda.2014.03.018

• Mixture of regressions
• Variable selection
• Particle Markov chain Monte Carlo

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.

• 出版日期2014-9