Empirical likelihood-based inference in a partially linear model for longitudinal data

Liugen Xue*, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

A partially linear model with longitudinal data is considered, empirical likelihood to inference for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the parameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.

Original languageEnglish
Pages (from-to)115-130
Number of pages16
JournalScience in China, Series A: Mathematics, Physics, Astronomy
Volume51
Issue number1
DOIs
Publication statusPublished - Jan 2008

Scopus Subject Areas

  • Mathematics(all)

User-Defined Keywords

  • Confidence region
  • Empirical likelihood
  • Longitudinal data
  • Partially linear model

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