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Misspecified Restriction in Partial Linear Models

  • Mohammad ArashiEmail author
  • Toktam Valizadeh
Conference paper
  • 77 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1001)

Abstract

In regression modeling, when there are some prior information (restrictions) about regression coefficients, restricted estimators perform well. In the context of partial linear model (PLM), we assert restrictions must be of semiparametric form, instead of commonly used parametric ones. Under the semiparametric restriction, the estimate of regression parameters is obtained, when multicollinearity exists and a closed-form mean squared error (MSE) is derived. The results are supported by an extensive numerical study. We demonstrate misspecified restriction in the PLM may cause larger MSE values.

Keywords

Misspecified restriction Multicollinearity Partial linear model Ridge estimator Regression models 

Notes

Acknowledgements

The work of M. Arashi is based upon research supported in part by the National Research Foundation of South Africa grant (Re:IFR170227223754 No. 109214).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Statistics, Faculty of Mathematical SciencesShahrood University of TechnologyShahroudIran
  2. 2.Department of Statistics, Faculty of Natural and Agricultural SciencesUniversity of PretoriaPretoriaSouth Africa
  3. 3.Department of Statistics, School of Mathematics and Computer ScienceAmirKabir University of TechnologyTehranIran
  4. 4.Member of Neuro Statistics Group of NIAG (Neuro Imagining and Analysis Group)TehranIran

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