Wavelet estimation in varying-coefficient partially linear regression models

Xian Zhou*, Jinhong You

*Corresponding author for this work

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

This paper is concerned with the estimation of a varying-coefficient partially linear regression model that is frequently used in statistical modeling. We first construct estimators of the parametric components and the error variance by a wavelet procedure and establish their asymptotic normalities under weaker conditions than those assumed in the previous literature. Then we propose appropriate estimators for the functions characterizing the nonlinear part of the model and derive their convergence rates. Furthermore, we present consistent estimators for the asymptotic (co)variances of the parametric components and error variance estimators as well. These results can be used to make asymptotically valid statistical inference.

Original languageEnglish
Pages (from-to)91-104
Number of pages14
JournalStatistics and Probability Letters
Volume68
Issue number1
DOIs
Publication statusPublished - 1 Jun 2004
Externally publishedYes

Keywords

  • Asymptotic normality
  • Consistency
  • Least-squares estimation
  • Partially linear regression model
  • Varying-coefficient
  • Wavelet

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