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The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture. (English) Zbl 1414.62268

Summary: In this paper we introduce a new method to the cluster analysis of longitudinal data focusing on the determination of uncertainty levels for cluster memberships. The method uses the Dirichlet-\(t\) distribution which notably utilizes the robustness feature of the student-\(t\) distribution in the framework of a Bayesian semi-parametric approach together with robust clustering of subjects evaluates the uncertainty level of subjects memberships to their clusters. We let the number of clusters and the uncertainty levels be unknown while fitting Dirichlet process mixture models. Two simulation studies are conducted to demonstrate the proposed methodology. The method is applied to cluster a real data set taken from gene expression studies.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62J05 Linear regression; mixed models
62G08 Nonparametric regression and quantile regression
62M99 Inference from stochastic processes

Software:

BUGS
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References:

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