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, va...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.11261025,11561075the Natural Science Foundation of Yunnan Province under Grant No.2016FB005the Program for Middle-aged Backbone Teacher,Yunnan University
文摘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.
