Variable selection for skew-normal mixture of joint location and scale models  

作　　者：WU Liu-cang YANG Song-qin TAO Ye 

机构地区：[1]Faculty of Science,Kunming University of Science and Technology,Kunming 650093,China [2]School of Economics and Statistics,Guangzhou University,Guangzhou 510006,China

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2021年第4期475-491,共17页高校应用数学学报（英文版）（B辑）

基　　金：Supported by the National Natural Science Foundation of China(11861041).

摘　　要：Although there are many papers on variable selection methods based on mean model in the nite mixture of regression models,little work has been done on how to select signi cant explanatory variables in the modeling of the variance parameter.In this paper,we propose and study a novel class of models:a skew-normal mixture of joint location and scale models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population.The problem of variable selection for the proposed models is considered.In particular,a modi ed Expectation-Maximization(EM)algorithm for estimating the model parameters is developed.The consistency and the oracle property of the penalized estimators is established.Simulation studies are conducted to investigate the nite sample performance of the proposed methodolo-gies.An example is illustrated by the proposed methodologies.

关 键 词：heterogeneous population skew-normal(SN)distribution mixture of joint location and scale models variable selection EM algorithm 

分 类 号：O212.1[理学—概率论与数理统计]