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Multiple‐Group Logistic Regression Diagnostics

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  • E. Lesaffre
  • A. Albert

Abstract

The regression diagnostics introduced by Pregibon for the dichotomous logistic model are extended to multiple groups viewed as a multivariate generalized linear model. We develop diagnostics which measure the influence of each observation on the performance of the estimated classification rule, thus highlighting possible multivariate outlying and/or influential observations. As an illustration, the diagnostics are applied to enzyme data from liver disease patients. It is shown that multivariate regression diagnostics constitute an indispensable tool for the practitioner to construct appropriate polychotomous logistic models.

Suggested Citation

  • E. Lesaffre & A. Albert, 1989. "Multiple‐Group Logistic Regression Diagnostics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(3), pages 425-440, November.
    • Handle: RePEc:bla:jorssc:v:38:y:1989:i:3:p:425-440
    DOI: 10.2307/2347731
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    Cited by:

    1. Miron, Julien & Poilane, Benjamin & Cantoni, Eva, 2022. "Robust polytomous logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    2. Pouya Gholizadeh & Behzad Esmaeili, 2020. "Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors," IJERPH, MDPI, vol. 17(13), pages 1-24, July.

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