Breakdown Point and Computation of Trimmed Likelihood Estimators in Generalized Linear Models

Breakdown Point and Computation of Trimmed Likelihood Estimators
in Generalized Linear Models
Neyko M. Neykov1, Christine H. Muller2

Abstract
A review of the studies concerning the finite sample breakdown point (BP) of the trimmed likelihood (TL) and related estimators based on the d–fullness technique of Vandev [28], and Vandev and Neykov [31] is made. In particular, the BP of these estimators in the frame of the generalized linear models (GLMs) depends on the trimming proportion and the quantity N(X) introduced by M¨uller [17]. A faster iterative algorithm based on resampling techniques for derivation of the TLE is developed. Examples of real and artificial data in the context of grouped logistic and log-linear regression models are used to illustrate the properties of the TLE.

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Breakdown Point and Computation of Trimmed Likelihood Estimators
in Generalized Linear Models