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Robust variable selection in finite mixture of regression models using the t distribution

Author

Listed:
  • Lin Dai
  • Junhui Yin
  • Zhengfen Xie
  • Liucang Wu

Abstract

Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression error. Such assumptions are unsuitable for a set of data containing a group or groups of observations with heavy tails and outliers. In this paper, we introduce a robust variable selection procedure for FMR models using the t distribution. With appropriate selection of the tuning parameters, the consistency and the oracle property of the regularized estimators are established. To estimate the parameters of the model, we develop an EM algorithm for numerical computations and a method for selecting tuning parameters adaptively. The parameter estimation performance of the proposed model is evaluated through simulation studies. The application of the proposed model is illustrated by analyzing a real data set.

Suggested Citation

  • Lin Dai & Junhui Yin & Zhengfen Xie & Liucang Wu, 2019. "Robust variable selection in finite mixture of regression models using the t distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(21), pages 5370-5386, November.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:21:p:5370-5386
    DOI: 10.1080/03610926.2018.1513143
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    Cited by:

    1. Jennifer S. K. Chan & S. T. Boris Choy & Udi Makov & Ariel Shamir & Vered Shapovalov, 2022. "Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data," Risks, MDPI, vol. 10(4), pages 1-10, April.

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