کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1145141 | 1489648 | 2016 | 14 صفحه PDF | دانلود رایگان |
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 mm-dependent and ββ-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.
Journal: Journal of Multivariate Analysis - Volume 152, December 2016, Pages 1–14