Concave group methods for variable selection and estimation in high-dimensional varying coefficient models

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	Concave group methods for variable selection and estimation in high-dimensional varying coefficient models
其他题名	Concave group methods for variable selection and estimation in high-dimensional varying coefficient models
	Yang GuangRen 1; Huang Jian 3; Zhou Yong 2
	2014
发表期刊	Science China Mathematics,
卷号	57 期号:10 页码:2073-2090
其他摘要	Abstract(#br)The varying-coefficient model is flexible and powerful for modeling the dynamic changes of regression coefficients. We study the problem of variable selection and estimation in this model in the sparse, high-dimensional case. We develop a concave group selection approach for this problem using basis function expansion and study its theoretical and empirical properties. We also apply the group Lasso for variable selection and estimation in this model and study its properties. Under appropriate conditions, we show that the group least absolute shrinkage and selection operator (Lasso) selects a model whose dimension is comparable to the underlying model, regardless of the large number of unimportant variables. In order to improve the selection results, we show that the group... minimax concave penalty (MCP) has the oracle selection property in the sense that it correctly selects important variables with probability converging to one under suitable conditions. By comparison, the group Lasso does not have the oracle selection property. In the simulation parts, we apply the group Lasso and the group MCP. At the same time, the two approaches are evaluated using simulation and demonstrated on a data example.展开 ?收缩
收录类别	CSCD
语种	英语
资助项目	[National Natural Science Foundation of China] ; [State Key Program of National Natural Science Foundation of China] ; [National Center for Mathematics and Interdisciplinary Sciences (NCMIS)] ; [Shanghai Leading Academic Discipline Project A in Ranking Top of Shanghai University of Finance and Economics (IRTSHUFE)] ; [Scientific Research Innovation Fund]
CSCD记录号	CSCD:5217660
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/52895
专题	中国科学院数学与系统科学研究院
作者单位	1.暨南大学 2.上海财经大学 3.艾奥瓦大学 4.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Yang GuangRen,Huang Jian,Zhou Yong. Concave group methods for variable selection and estimation in high-dimensional varying coefficient models[J]. Science China Mathematics,,2014,57(10):2073-2090.
APA	Yang GuangRen,Huang Jian,&Zhou Yong.(2014).Concave group methods for variable selection and estimation in high-dimensional varying coefficient models.Science China Mathematics,,57(10),2073-2090.
MLA	Yang GuangRen,et al."Concave group methods for variable selection and estimation in high-dimensional varying coefficient models".Science China Mathematics, 57.10(2014):2073-2090.