中文摘要：

可变选择是在现代统计的一个重要研究话题，传统的可变选择方法能仅仅选择平均数模型并且(或) 变化模型，并且不能被用来选择关节平均数，变化和偏斜度当模特儿。在这篇论文，作者建议联合地点，规模和偏斜度模型数据什么时候在考虑下面设定，包含不对称的结果，并且为我们的建议模型考虑可变选择的问题。基于一个有效统一惩罚可能性的方法，一致性和惩罚评估者的神谕性质被建立。作者发展为建议联合模型的可变选择过程，它能同时高效地在地点模型估计并且选择重要变量，可伸缩模型和偏斜度模型。模拟学习并且身体团索引数据分析被介绍说明建议方法。

英文摘要：

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 loca- tion, 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.