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Identification of multiple influential observations in logistic regression

Author

Listed:
  • A. A. M. Nurunnabi
  • A.H.M. Rahmatullah Imon
  • M. Nasser

Abstract

The identification of influential observations in logistic regression has drawn a great deal of attention in recent years. Most of the available techniques like Cook's distance and difference of fits (DFFITS) are based on single-case deletion. But there is evidence that these techniques suffer from masking and swamping problems and consequently fail to detect multiple influential observations. In this paper, we have developed a new measure for the identification of multiple influential observations in logistic regression based on a generalized version of DFFITS. The advantage of the proposed method is then investigated through several well-referred data sets and a simulation study.

Suggested Citation

  • A. A. M. Nurunnabi & A.H.M. Rahmatullah Imon & M. Nasser, 2010. "Identification of multiple influential observations in logistic regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1605-1624.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1605-1624
    DOI: 10.1080/02664760903104307
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    Cited by:

    1. A.A.M. Nurunnabi & Ali S. Hadi & A.H.M.R. Imon, 2014. "Procedures for the identification of multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1315-1331, June.
    2. Li-Chu Chien, 2013. "Multiple deletion diagnostics in beta regression models," Computational Statistics, Springer, vol. 28(4), pages 1639-1661, August.

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