Shrinkage estimation applied to a semi-nonparametric regression model

Zareamoghaddam Hossein (), Ahmed Syed E. () and Provost Serge B. ()
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Zareamoghaddam Hossein: Department of Statistical and Actuarial Sciences, The University of Western Ontario, London, Ontario, Canada
Ahmed Syed E.: Mathematics and Statistics, Brock University, St. Catahrines, Ontario, L2S3A1, Canada
Provost Serge B.: Department of Statistical and Actuarial Sciences, The University of Western Ontario, London, Ontario, Canada

The International Journal of Biostatistics, 2021, vol. 17, issue 1, 23-38

Abstract: Stein-type shrinkage techniques are applied to the parametric components of a semi-nonparametric regression model recently proposed by (Ma et al. 2015: 285–303). On the basis of an uncertain prior information (restrictions) about the parameters of interest, shrinkage techniques are shown to improve the accuracy of the model. The effectiveness of the proposed estimators are corroborated by a simulation study.

Keywords: local linear regression; multiple simple regression; semi-nonparametric model; shrinkage (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/ijb-2018-0109

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