Group-wise semiparametric modeling: A SCSE approach

Song Song, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalJournal of Multivariate Analysis
Publication statusPublished - 1 Dec 2016

Scopus Subject Areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Covariance estimation
  • Regularization
  • Semiparametrics
  • Sparsity
  • Thresholding
  • Variable clustering


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