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Variable selection in joint location, scale and skewness models of the skew-normal distribution

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Abstract

Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and (or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes, and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.

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Authors and Affiliations

  1. Department of Statistics, Yunnan University, Kunming, 650091, China

    Huiqiong Li

  2. Faculty of Science, Kunming University of Science and Technology, Kunming, 650093, China

    Liucang Wu & Ting Ma

Authors
  1. Huiqiong Li
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  2. Liucang Wu
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  3. Ting Ma
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Corresponding author

Correspondence to Huiqiong Li.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 11261025, 11561075, the Natural Science Foundation of Yunnan Province under Grant No. 2016FB005 and the Program for Middle-aged Backbone Teacher, Yunnan University.

This paper was recommended for publication by Editor SUN Liuquan.

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Li, H., Wu, L. & Ma, T. Variable selection in joint location, scale and skewness models of the skew-normal distribution. J Syst Sci Complex 30, 694–709 (2017). https://doi.org/10.1007/s11424-016-5193-2

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  • DOI: https://doi.org/10.1007/s11424-016-5193-2

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