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Robustness of ML Estimators of Location-Scale Mixtures

  • Christian Hennig
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The robustness of ML estimators for mixture models with fixed and estimated number of components s is investigated by the definition and computation of a breakdown point for mixture model parameters and by considering some artificial examples. The ML estimator of the Normal mixture model is compared with the approach of adding a “noise component” (Fraley and Raftery (1998)) and by mixtures of t-distributions (Peel and McLachlan (2000)). It turns out that the estimation of the number of mixture components is crucial for breakdown robustness. To attain robustness for fixed s, the addition of an improper noise component is proposed. A guideline to choose a lower scale bound is given.

Keywords

Mixture Component Noise Component Breakdown Point Normal Mixture Original Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Christian Hennig
    • 1
  1. 1.Fachbereich Mathematik - SPSTUniversität HamburgHamburgGermany

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