Robust Estimation in the Normal Mixture Model Based on Robust Clustering
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Publication:3631473
DOI10.1111/j.1467-9868.2008.00657.xOpenAlexW2027863679MaRDI QIDQ3631473
Agustín Mayo-Iscar, Carlos Matrán, Juan Antonio Cuesta-Albertos
Publication date: 10 June 2009
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.1467-9868.2008.00657.x
asymptoticsEM algorithminfluence functionbreakdown pointidentifiabilitytrimmed \(k\)-meanscensored maximum likelihoodmultivariate normal mixture model
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Cites Work
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- Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods
- On the convergence properties of the EM algorithm
- A constrained formulation of maximum-likelihood estimation for normal mixture distributions
- Trimmed \(k\)-means: An attempt to robustify quantizers
- Adaptively truncated maximum likelihood regression with asymmetric errors.
- A robust method for cluster analysis
- Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
- Breakdown points for maximum likelihood estimators of location-scale mixtures
- Weak convergence and empirical processes. With applications to statistics
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Robustness Properties of k Means and Trimmed k Means
- Mixture Models, Robustness, and the Weighted Likelihood Methodology
- On the Identifiability of Finite Mixtures
