标题:New efficient estimation and variable selection in models with single-index structure
作者:Wang, Kangning; Lin, Lu
作者机构:[Wang, Kangning] Chongqing Univ Arts & Sci, Dept Math, Chongqing, Peoples R China.; [Wang, Kangning] Chongqing Univ Arts & Sci, KLDAIP, Chongqing, P 更多
通讯作者:Wang, KN
通讯作者地址:[Wang, KN]Chongqing Univ Arts & Sci, Dept Math, Chongqing, Peoples R China.
来源:STATISTICS & PROBABILITY LETTERS
出版年:2014
卷:89
页码:58-64
DOI:10.1016/j.spl.2014.02.019
关键词:Linearity condition; Composite quantile regression; Adaptive LASSO;; Oracle property; Single-index structure
摘要:We propose a new efficient root-n consistent estimate for the direction of the index parameter vector in a class of models with single-index structure. To select the important predictors, we suggest using the adaptive LASSO and establish the oracle property. Simulation results also confirm the theoretical findings. (c) 2014 Elsevier B.V. All rights reserved.
收录类别:SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896513747&doi=10.1016%2fj.spl.2014.02.019&partnerID=40&md5=fccf62ced887f420cbe26df6a3b56d4b
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