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Multiple Deletions in Logistic Regression Models

Jung, Kang-Mo

  • Published : 2009.03.30

Abstract

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.

Keywords

Conditional deletions;logistic regression models;masking effects;multiple deletions;outliers;swamping effects

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