## 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.

Original language | English |
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Pages (from-to) | 84-97 |

Number of pages | 14 |

Journal | COMPUTATIONAL STATISTICS AND DATA ANALYSIS |

Volume | 77 |

DOIs | |

Publication status | Published - Sept 2014 |

## Keywords

- Mixture of regressions
- Variable selection
- Particle Markov chain Monte Carlo
- SEQUENTIAL MONTE-CARLO
- VARIABLE SELECTION
- FINITE MIXTURE
- MODELS