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Logistic regression diagnostics in ridge regression

Author

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  • M. Revan Özkale

    (Çukurova University)

  • Stanley Lemeshow

    (The Ohio State University)

  • Rodney Sturdivant

    (Azusa Pacific University)

Abstract

The adverse effects of multicollinearity and unusual observations are seen in logistic regression and attention had been given in the literature to each of these problems separately. However, multicollinearity and unusual observations can arise simultaneously in logistic regression. The objective of this paper is to propose the statistics for detecting the unusual observations in an ill-conditioned data set under the ridge logistic estimator. A numerical example and two Monte Carlo simulation studies are used to illustrate the methodology. The present investigation shows that ridge logistic estimation copes with unusual observations by downweighting their influence.

Suggested Citation

  • M. Revan Özkale & Stanley Lemeshow & Rodney Sturdivant, 2018. "Logistic regression diagnostics in ridge regression," Computational Statistics, Springer, vol. 33(2), pages 563-593, June.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0755-x
    DOI: 10.1007/s00180-017-0755-x
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    References listed on IDEAS

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    1. Jahufer, Aboobacker & Jianbao, Chen, 2009. "Assessing global influential observations in modified ridge regression," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 513-518, February.
    2. D. A. Williams, 1987. "Generalized Linear Model Diagnostics Using the Deviance and Single Case Deletions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 181-191, June.
    3. Preisser, John S. & Garcia, Daniel I., 2005. "Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 755-764, April.
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    Cited by:

    1. M. Revan Özkale & Engin Arıcan, 2019. "A first-order approximated jackknifed ridge estimator in binary logistic regression," Computational Statistics, Springer, vol. 34(2), pages 683-712, June.

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