Robust fitting of mixtures of GLMs by weighted likelihood

Luca Greco ()
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Luca Greco: University of Sannio

AStA Advances in Statistical Analysis, 2022, vol. 106, issue 1, No 2, 25-48

Abstract: Abstract Finite mixtures of generalized linear models are commonly fitted by maximum likelihood and the EM algorithm. The estimation process and subsequent inferential and classification procedures can be badly affected by the occurrence of outliers. Actually, contamination in the sample at hand may lead to severely biased fitted components and poor classification accuracy. In order to take into account the potential presence of outliers, a robust fitting strategy is proposed that is based on the weighted likelihood methodology. The technique exhibits a satisfactory behavior in terms of both fitting and classification accuracy, as confirmed by some numerical studies and real data examples.

Keywords: Classification; EM; GLM; Mixture; Outliers; Weighted likelihood; MSC 62F35; MSC 62G35; MSC 62H25; MSC 62H30 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10182-021-00402-y

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