Statistical inference for a single-index varying-coefficient model

Liugen Xue, Zhen Pang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

We investigate the estimators of parameters of interest for a single-index varying-coefficient model. To estimate the unknown parameter efficiently, we first estimate the nonparametric component using local linear smoothing, then construct an estimator of parametric component by using estimating equations. Our estimator for the parametric component is asymptotically efficient, and the estimator of nonparametric component has asymptotic normality and optimal uniform convergence rate. Our results provide ways to construct confidence regions for the involved unknown parameters. The finite-sample behavior of the new estimators is evaluated through simulation studies, and applications to two real data are illustrated.
Original languageEnglish
Pages (from-to)589-599
Number of pages11
JournalStatistics and Computing
Volume23
Issue number5
DOIs
Publication statusPublished - 1 Sep 2013
Externally publishedYes

Keywords

  • Asymptotic normality
  • Bandwidth
  • Confidence region
  • Local linear smoothing
  • Single-index varying-coefficient model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

Cite this