Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression.

摘要

We study optimally bounded score functions for estimating regression parameters in a generalized linear model. Our work extends results obtained by Krasker and Welsch (1982) for the linear model and provides a simple proof of Krasker and Welsch's 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. Keywords: Reprints. (kr)