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Copula analysis of mixture models

Chédin, Alain; Diday, Edwin; Billard, Lynne; Vrac, Mathieu (2012), Copula analysis of mixture models, Computational Statistics, 27, 3, p. 427-457. http://dx.doi.org/10.1007/s00180-011-0266-0

Type
Article accepté pour publication ou publié
Date
2012
Journal name
Computational Statistics
Volume
27
Number
3
Publisher
Springer
Pages
427-457
Publication identifier
http://dx.doi.org/10.1007/s00180-011-0266-0
Metadata
Show full item record
Author(s)
Chédin, Alain
Diday, Edwin
Billard, Lynne
Vrac, Mathieu
Abstract (EN)
Contemporary computers collect databases that can be too large for classical methods to handle. The present work takes data whose observations are distribution functions (rather than the single numerical point value of classical data) and presents a computational statistical approach of a new methodology to group the distributions into classes. The clustering method links the searched partition to the decomposition of mixture densities, through the notions of a function of distributions and of multi-dimensional copulas. The new clustering technique is illustrated by ascertaining distinct temperature and humidity regions for a global climate dataset and shows that the results compare favorably with those obtained from the standard EM algorithm method.
Subjects / Keywords
Mixture model; Estimation; Data distributions; Dynamical clustering; Copulas; Classification of distributions

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