Efficient penalized estimating method in the partially varying-coefficient single-index model

Zhensheng Huang, Bingqing Lin, Fan Feng, Zhen Pang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In this paper, penalized estimating equations are proposed to estimate the index parametric components, which is of primary interest, in the partially varying-coefficient single-index models (PVCSIMs). Although some procedures have been developed to estimate the index parameter in PVCSIM, the problem of how to conduct variable selection for the index in such models has not been addressed to date. To solve this problem, we propose a class of efficient penalized estimating equations, which combine the smoothly clipped absolute deviation (SCAD) penalty and a stepwise estimation method. The proposed method can simultaneously select significant variables in the index and estimate the nonzero smooth coefficient parameters. Under suitable conditions, we establish the theoretical properties of our penalized estimating procedure, including the oracle properties and the asymptotic normality for the resulting penalized estimation. We evaluate the performance of the proposed method by using Monte Carlo simulations and the application to a real dataset.
Original languageEnglish
Pages (from-to)189-200
Number of pages12
JournalJournal of Multivariate Analysis
Volume114
Issue number1
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Keywords

  • Least-squared method
  • Penalized estimating equations
  • Reparametrization method
  • SCAD
  • Varying-coefficient single-index model

ASJC Scopus subject areas

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

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