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