Testing structural change in partially linear single-index models with error-prone linear covariates

Zhensheng Huang, Zhen Pang, Tao Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Motivated by an analysis of a real data set from Duchenne Muscular Dystrophy (Andrews and Herzberg, 1985), we propose a new test of structural change for a class of partially linear single-index models with error-prone linear covariates. Based on the local linear estimation for the unknowns in these semiparametric models, we develop a new generalized F-test statistics for the nonparametric part in the partially linear single-index models with error-prone linear covariates. Asymptotic properties of the newly proposed test statistics are proved to follow asymptotically the chi-squared distribution. The new Wilks' phenomenon is unveiled in a class of semiparametric measure error models. Simulations are conducted to examine the performance of our proposed method. The simulation results are consistent with our theoretical findings. Real data examples are used to illustrate the proposed methodology.
Original languageEnglish
Pages (from-to)121-133
Number of pages13
JournalComputational Statistics and Data Analysis
Volume59
Issue number1
DOIs
Publication statusPublished - 1 Mar 2013
Externally publishedYes

Keywords

  • Chi-squared distribution
  • F-type test
  • Local linear method
  • Measure error
  • Single-index model

ASJC Scopus subject areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

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