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A quadratic bootstrap method and improved estimation in logistic regression

Author

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  • Claeskens, Gerda
  • Aerts, Marc
  • Molenberghs, Geert

Abstract

This paper presents a quadratic one-step bootstrap method for binary response data. Rather than resampling from the original sample, the proposed method resamples summands appearing in the quadratic approximation of the estimates. It enjoys the same computational simplicity as its linear analogue while being more accurate. Moreover it allows the construction of a bias corrected estimator and improved confidence intervals. A small simulation study illustrates the improved finite sample behaviour for binary response data.

Suggested Citation

  • Claeskens, Gerda & Aerts, Marc & Molenberghs, Geert, 2003. "A quadratic bootstrap method and improved estimation in logistic regression," Statistics & Probability Letters, Elsevier, vol. 61(4), pages 383-394, February.
    • Handle: RePEc:eee:stapro:v:61:y:2003:i:4:p:383-394
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    References listed on IDEAS

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    1. MacKinnon, James G. & Smith Jr., Anthony A., 1998. "Approximate bias correction in econometrics," Journal of Econometrics, Elsevier, vol. 85(2), pages 205-230, August.
    2. Moulton, Lawrence H. & Zeger, Scott L., 1991. "Bootstrapping generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 11(1), pages 53-63, January.
    3. Aerts, Marc & Claeskens, Gerda, 2001. "Bootstrap tests for misspecified models, with application to clustered binary data," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 383-401, May.
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    Cited by:

    1. Jason S. Bergtold & Elizabeth A. Yeager & Allen M. Featherstone, 2018. "Inferences from logistic regression models in the presence of small samples, rare events, nonlinearity, and multicollinearity with observational data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 528-546, February.

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