Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression.
Technical rept. Sep 84-Aug 85,
NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF STATISTICS
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This document studied optimally bounded score functions for estimating regression parameters in a generalized linear model. This work extends results obtained by Krasker Welsch 1982 for the linear model and provides a simple proof of Krasker and Welschs first order condition for strong optimality. The application of these results to logistic regression is studied in some detail with an example given comparing the bounded influence estimator with maximum likelihood. Additional keywords Outliers Robustness Influentral points. Author
- Statistics and Probability