Robust modeling for inference from generalized linear model classes. (English) Zbl 1334.62142

Noh, Maengseok; Lee, Youngjo

J. Am. Stat. Assoc. 102, No. 479, 1059-1072 (2007).

Summary: Generalized linear models (GLMs) are widely used for data analysis; however, their maximum likelihood estimators can be sensitive to outliers. We propose new statistical models that allow robust inferences from the GLM class of models, including Poisson and binomial GLMs, and their extension to generalized linear mixed models. The likelihood score equations from the new models give estimators with bounded influence, so that the resulting estimators are robust against outliers while maintaining high efficiency in the absence of outliers.

MSC:

62J12	Generalized linear models (logistic models)
62F35	Robustness and adaptive procedures (parametric inference)

Keywords:

bounded influence; generalized linear mixed model; generalized linear model; robustness