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|a 10.1080/02664763.2019.1692795
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|a eng
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|a Park, Seongoh
|e verfasserin
|4 aut
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|a Clustering of longitudinal interval-valued data via mixture distribution under covariance separability
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 16.07.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2019 Informa UK Limited, trading as Taylor & Francis Group.
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|a We consider the clustering of repeatedly measured 'min-max' type interval-valued data. We read the data as matrix variate data and assume the covariance matrix is separable for the model-based clustering (M-clustering). The use of a separable covariance matrix introduces several advantages in M-clustering, which include fewer samples required for a valid procedure. In addition, the numerical study shows that this structured matrix allows us to find the correct number of clusters more accurately compared to other commonly assumed covariance matrices. We apply the M-clustering with various covariance structures to clustering the longitudinal blood pressure data from the National Heart, Lung, and Blood Institute Growth and Health Study (NGHS)
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|a Journal Article
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|a 62-07
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|a 62H30
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|a Clustering
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|a M-clustering
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|a interval-valued data
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|a longitudinal data
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|a matrix variate data
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|a separable covariance matrix
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|a Lim, Johan
|e verfasserin
|4 aut
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|a Choi, Hyejeong
|e verfasserin
|4 aut
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|a Kwak, Minjung
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 47(2020), 10 vom: 14., Seite 1739-1756
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|x 0266-4763
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|g volume:47
|g year:2020
|g number:10
|g day:14
|g pages:1739-1756
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|u http://dx.doi.org/10.1080/02664763.2019.1692795
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