Robust fitting of mixtures using the trimmed likelihood estimator
DOI10.1016/j.csda.2006.12.024zbMath1328.62033OpenAlexW2112905646MaRDI QIDQ135707
R. Dimova, P. Filzmoser, P. Neytchev, N. Neykov, J. Martínez
Publication date: September 2007
Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2006.12.024
maximum likelihood estimatorbreakdown pointoutlier detectiontrimmed likelihood estimatorrobust clusteringfinite mixtures of distributions
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Related Items (66)
Uses Software
Cites Work
- Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator
- Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms
- Clusters, outliers, and regression: Fixed point clusters
- Breakdown points of trimmed likelihood estimators and related estimators in generalized linear models.
- A robust method for cluster analysis
- About Regression Estimators with High Breakdown Point
- Mixture Models, Robustness, and the Weighted Likelihood Methodology
- Robust Statistics
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