Group-wise semiparametric modeling: A SCSE approach

Song Song and Lixing Zhu

Journal of Multivariate Analysis, 2016, vol. 152, issue C, 1-14

Abstract: 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 m-dependent and β-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.

Keywords: Covariance estimation; Regularization; Sparsity; Thresholding; Semiparametrics; Variable clustering (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:152:y:2016:i:c:p:1-14

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DOI: 10.1016/j.jmva.2016.07.006

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