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Bootstrapping multiple linear regression after variable selection

  • Lasanthi C. R. Pelawa Watagoda
  • David J. OliveEmail author
Regular Article
  • 12 Downloads

Abstract

This paper suggests a method for bootstrapping the multiple linear regression model \(Y = \beta _1 + \beta _2 x_2 + \cdots + \beta _p x_p + e\) after variable selection. We develop asymptotic theory for some common least squares variable selection estimators such as forward selection with \(C_p\). Then hypothesis testing is done using three confidence regions, one of which is new. Theory suggests that the three confidence regions tend to have coverage at least as high as the nominal coverage if the sample size is large enough.

Keywords

Bagging Confidence region Forward selection 

Notes

Acknowledgements

The authors thank the Editor and two referees for their work.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematical SciencesAppalachian State UniversityBooneUSA
  2. 2.Department of MathematicsSouthern Illinois UniversityCarbondaleUSA

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