Adaptive estimation in partially linear autoregressive models

Jiti Gao, Thomas Yee

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)


The authors consider a partially linear autoregressive model and construct kernel-based estimates for both the parametric and nonparametric components. They propose an estimation procedure for the model and illustrate it through simulated and real data. Their work shows that the proposed estimation procedure not only has good asymptotic properties but also works well numerically. It also suggests that a partially linear autoregression is more appropriate than a completely nonparametric autoregression for some sets of data.

Original languageEnglish
Pages (from-to)571-586
Number of pages16
JournalCanadian Journal of Statistics
Issue number3
Publication statusPublished - 1 Jan 2000


  • Adaptive estimation
  • Dependent process
  • Nonlinear time series
  • Partially linear autoregression
  • Strict stationarity

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