Group-wise semiparametric modeling: A SCSE approach

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• 作者：Song Song^a ; ^{ssoonngg123@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Lixing Zhu^b ; ^c ; ^{lzhu@hkbu.edu.hk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：62J10 ; 62G08 ; 62H30
• 刊名：Journal of Multivariate Analysis
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：152
• 期：Complete
• 页码：1-14
• 全文大小：1010 K

文摘

This paper is motivated by the modeling of a high-dimensional dataset via group-wise information on explanatory variables. A three-step algorithm is suggested for group-wise semiparametric modeling: (i) screening to reduce dimensionality; (ii) clustering according to grouped explanatory variables; (iii) sign-constraints-based estimation for coefficients to produce meaningful interpretations. As a justification, under the setup of 16e65ad" title="Click to view the MathML source">m$m$-dependent and β$\beta$-mixing processes, the interplay between the estimator’s convergence rate and the temporal dependence level is quantified and a cross-validation result about the resampling scheme for threshold selection is also proved. This method is evaluated in finite-sample cases through a Monte Carlo experiment, and illustrated with an analysis of the US consumer price index.