Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 52, Issue 1, Pages 299-308Publisher
ELSEVIER
DOI: 10.1016/j.csda.2006.12.024
Keywords
maximum likelihood estimator; trimmed likelihood estimator; breakdown point; finite mixtures of distributions; robust clustering; outlier detection
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The maximum likelihood estimator (MLE) has commonly been used to estimate the unknown parameters in a finite mixture of distributions. However, the MLE can be very sensitive to outliers in the data. In order to overcome this the trimmed likelihood estimator (TLE) is proposed to estimate mixtures in a robust way. The superiority of this approach in comparison with the MLE is illustrated by examples and simulation studies. Moreover, as a prominent measure of robustness, the breakdown point (BDP) of the TLE for the mixture component parameters is characterized. The relationship of the TLE with various other approaches that have incorporated robustness in fitting mixtures and clustering are also discussed in this context. (c) 2007 Elsevier B.V. All rights reserved.
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