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Variable selection in finite mixture of regression models using the skew-normal distribution

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  • Junhui Yin
  • Liucang Wu
  • Lin Dai

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 asymmetric behavior. In this paper, we introduce a variable selection procedure for FMR models using the skew-normal distribution. With appropriate choice of the tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. To estimate the parameters of the model, a modified EM algorithm for numerical computations is developed. The methodology is illustrated through numerical experiments and a real data example.

Suggested Citation

  • Junhui Yin & Liucang Wu & Lin Dai, 2020. "Variable selection in finite mixture of regression models using the skew-normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(16), pages 2941-2960, December.
  • Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:2941-2960
    DOI: 10.1080/02664763.2019.1709051
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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.
    2. Yuanyuan Ju & Yan Yang & Mingxing Hu & Lin Dai & Liucang Wu, 2022. "Bayesian Influence Analysis of the Skew-Normal Spatial Autoregression Models," Mathematics, MDPI, vol. 10(8), pages 1-19, April.

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