A Bayesian mixture of lasso regressions with t-errors

Alberto Cozzini, Ajay Jasra*, Giovanni Montana, Adam Persing

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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 languageEnglish
Pages (from-to)84-97
Number of pages14
JournalCOMPUTATIONAL STATISTICS AND DATA ANALYSIS
Volume77
DOIs
Publication statusPublished - Sept 2014

Keywords

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

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