Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61882
Title: Principal components regression estimator of the parameters in partially linear models
Authors: Liu, C 
Guo, S
Wei, C
Keywords: Multicollinearity
Partially linear models
Principalcomponents regression
Profile least-squares approach
Issue Date: 2016
Publisher: Taylor & Francis
Source: Journal of statistical computation and simulation, 2016, v. 86, no. 15, p. 3127-3133 How to cite?
Journal: Journal of statistical computation and simulation 
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.
URI: http://hdl.handle.net/10397/61882
ISSN: 0094-9655 (print)
1563-5163 (online)
DOI: 10.1080/00949655.2016.1151516
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