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Bootstrapping multiple linear regression after variable selection

Author

Listed:
  • Lasanthi C. R. Pelawa Watagoda

    (Appalachian State University)

  • David J. Olive

    (Southern Illinois University)

Abstract

This paper suggests a method for bootstrapping the multiple linear regression model $$Y = \beta _1 + \beta _2 x_2 + \cdots + \beta _p x_p + e$$ Y = β 1 + β 2 x 2 + ⋯ + β p x p + e after variable selection. We develop asymptotic theory for some common least squares variable selection estimators such as forward selection with $$C_p$$ 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.

Suggested Citation

  • Lasanthi C. R. Pelawa Watagoda & David J. Olive, 2021. "Bootstrapping multiple linear regression after variable selection," Statistical Papers, Springer, vol. 62(2), pages 681-700, April.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:2:d:10.1007_s00362-019-01108-9
    DOI: 10.1007/s00362-019-01108-9
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    References listed on IDEAS

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    6. David J. Olive, 2018. "Applications of hyperellipsoidal prediction regions," Statistical Papers, Springer, vol. 59(3), pages 913-931, September.
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