Principal components regression estimator of the parameters in partially linear models

Chunling Liu, Shuang Guo, Chuanhua Wei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


As a compromise between parametric regression and non-parametric regression models, partially linear models are frequently used in statistical modelling. This paper is concerned with the estimation of partially linear regression model in the presence of multicollinearity. Based on the profile least-squares approach, we propose a novel principal components regression (PCR) estimator for the parametric component. When some additional linear restrictions on the parametric component are available, we construct a corresponding restricted PCR estimator. Some simulations are conducted to examine the performance of our proposed estimators and the results are satisfactory. Finally, a real data example is analysed.
Original languageEnglish
Pages (from-to)3127-3133
Number of pages7
JournalJournal of Statistical Computation and Simulation
Issue number15
Publication statusPublished - 12 Oct 2016


  • Multicollinearity
  • partially linear models
  • principalcomponents regression
  • profile least-squares approach

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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