Abstract
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 language | English |
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Pages (from-to) | 3127-3133 |
Number of pages | 7 |
Journal | Journal of Statistical Computation and Simulation |
Volume | 86 |
Issue number | 15 |
DOIs | |
Publication status | Published - 12 Oct 2016 |
Keywords
- 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