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A Bayesian mixture of lasso regressions with t-errors

Author

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

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.

Suggested Citation

  • Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.
    • Handle: RePEc:eee:csdana:v:77:y:2014:i:c:p:84-97
    DOI: 10.1016/j.csda.2014.03.018
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yifan & Fong, Duncan K.H. & DeSarbo, Wayne S., 2021. "A generalized ordinal finite mixture regression model for market segmentation," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 1055-1072.
    2. Lee, Kuo-Jung & Feldkircher, Martin & Chen, Yi-Chi, 2021. "Variable selection in finite mixture of regression models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

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