A Case Study of two Clustering Methods based on Maximum Likelihood

DOI10.1111/j.1467-9574.1979.tb00665.xzbMath0404.62035OpenAlexW2064903690MaRDI QIDQ4189977

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Publication date: 1979

Published in: Statistica Neerlandica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9574.1979.tb00665.x

zbMATH Keywords

UpdatingCase StudyMaximum LikelihoodApplicationMedical DataAllocation ProcedureClassification ProblemClustering Methods

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

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Cites Work

• 389: Separating Mixtures of Normal Distributions
• Iterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis
• Population mixture models and clustering algorithms
• Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations
• Bayesian cluster analysis
• The efficiency of a linear discriminant function based on unclassified initial samples
• Asymptotic behaviour of classification maximum likelihood estimates
• Classification and Estimation in Analysis of Variance Problems
• Estimating the components of a mixture of normal distributions