Copula analysis of mixture models
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. Copyright Springer-Verlag 2012
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Volume (Year): 27 (2012)
Issue (Month): 3 (September)
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- Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
- Phipps Arabie & J. Carroll, 1980. "Mapclus: A mathematical programming approach to fitting the adclus model," Psychometrika, Springer;The Psychometric Society, vol. 45(2), pages 211-235, June.
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- Gildas Brossier, 1990. "Piecewise hierarchical clustering," Journal of Classification, Springer;The Classification Society, vol. 7(2), pages 197-216, September.
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