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Model-based linear clustering. (English. French summary) Zbl 1349.62288
Summary: The authors propose a profile likelihood approach to linear clustering which explores potential linear clusters in a data set. For each linear cluster, an errors-in-variables model is assumed. The optimization of the derived profile likelihood can be achieved by an EM algorithm. Its asymptotic properties and its relationships with several existing clustering methods are discussed. Methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purposes.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62J05 Linear regression; mixed models
62F12 Asymptotic properties of parametric estimators
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