Association Journal of CSIAM
Supervised by Ministry of Education of PRC
Sponsored by Xi'an Jiaotong University
ISSN 1005-3085  CN 61-1269/O1

Chinese Journal of Engineering Mathematics ›› 2023, Vol. 40 ›› Issue (3): 381-397.doi: 10.3969/j.issn.1005-3085.2023.03.004

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Variable Selection in Mode Regression Models Using the Mixture Skew-normal Data

ZENG Xin,   WU Liucang,   JU Yuanyuan   

  1. Faculty of Science, Kunming University of Science and Technology, Kunming 650093
  • Received:2020-09-25 Accepted:2021-05-31 Online:2023-06-15 Published:2023-08-15
  • Contact: Y. Ju. E-mail address: jundeyy@126.com
  • Supported by:
    The National Natural Science Foundation of China (11861041); the Academic and Technology Innovation Foundation of Kunming University of Science and Technology (2020YB208).

Abstract:

Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The existing studies on FMR models mainly base on the normality ass-umption of regression error. However, this assumption is not suitable for studying asymmetric data. The performance of the mode is better than that of the mean for skewed data. This paper proposes a variable selection method for mixture of mode regression models basing on the skew-normal distribution. The consistency and the Oracle property are proved. A modified EM algorithm is developed to estimate the parameters in the model. Simulation studies are conducted to investigate the performance of the proposed methodologies. A real example is further provided to investigate the performance of the proposed methodologies.

Key words: mixture of skew-normal data, mode regression models, variable selection, EM algorithm

CLC Number: