Multiple Deletions in Logistic Regression Models

  • Jung, Kang-Mo (Dept. of Informatics and Statistics, Kunsan National Univ.)
  • Published : 2009.03.30


We extended the results of Roy and Guria (2008) to multiple deletions in logistic regression models. Since single deletions may not exactly detect outliers or influential observations due to swamping effects and masking effects, it needs multiple deletions. We developed conditional deletion diagnostics which are designed to overcome problems of masking effects. We derived the closed forms for several statistics in logistic regression models. They give useful diagnostics on the statistics.



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