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Variable selection of varying dispersion student-t regression models

ISSN号：1009-6124
期刊名称：Journal of Systems Science and Complexity
时间：2015.8
页码：961-977
分类：O212.1[理学—概率论与数理统计;理学—数学] TN911.7[电子电信—通信与信息系统;电子电信—信息与通信工程]
作者机构：[1]School of ScienCe, Nantong University, Nantong 226007, China., [2]School of Finance and Statistics, East China Normal University, Shanghai 200241, China, Department of Mathematics, Shanxi Datong University, Datong 037009, China.
相关基金：The research was supported in part by the National Natural Science Foundation of China under Grant Nos. 11171112, 11101114, 11201190, the National Statistical Science Research Major Program of China under Grant No. 2011LZ051, the 111 Project of China under Grant No. B14019, the Doctoral Fund of Ministry of Edu- cation of China under Grant No. 20130076110004, the Natural Science Project of Jiangsu Province Education Department under Grant No. 13KJBl10024, the Natural Science Fund of Nantong University under Grant No. 13ZY001, and the Research Project of Social Science and Humanity Fund of the Ministry of Education under Grant No. 14YJC910007.
相关项目：含指标项半参数分位数回归模型的统计分析

关键词：回归模型, 变量选择, 分散, 估计方法, 回归系数, 统计建模, 调整参数, 甲骨文, LASSO, SCAD, Student-t distribution, variable selection, varying dispersion.

中文摘要：

Student-t 回归模型是正常模型的有用延期，它能被用于与重尾巴或工具和回归系数的孤立点和 providesrobust 评价包含错误的数据集合的统计建模。在这份报纸，变化分散 Student-t 回归模型被讨论，在哪个平均数和分散取决于解释变量。利息的问题同时在平均数和分散模型两个都是精选重要变量。罐头同时选择重要变量的一个统一过程被给。与调节参数的适当选择，一致性和调整评估者的神谕性质被建立。模拟学习和二个真实数据例子被用来说明建议方法论。

英文摘要：

The Student-t regression model is a useful extension of the normal model, which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and provides robust estimation of means and regression coefficients. In this paper, the varying dispersion Student-t regression model is discussed, in which both the mean and the dispersion depend upon explanatory variables. The problem of interest is simultaneously select significant variables both in mean and dispersion model. A unified procedure which can simultaneously select significant variable is given. With appropriate selection of the tuning parameters; the consistency and the oracle property of the regularized estimators are established. Both the simulation study and two real data examples are used to illustrate the proposed methodologies.

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