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• W2002153815 abstract "The article extends the REBMIX to multivariate data. Random variables may follow normal, lognormal, or Weibull parametric families and should be independent within components. The initial weights and component parameters are not required. Preprocessing of observations folows the histogram, Parzen window, or k-nearest neighbor approach. The number of components, weights, and component parameters are gained iteratively by using information measures of the distance, such as the total of positive relative deviations and the information criterion. The number of classes or the number of the nearest neighbors can be optimized, as well. The REBMIX software is available on http://www.fs.uni-lj.si/lavek." @default.
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• W2002153815 date "2011-04-15" @default.
• W2002153815 modified "2023-09-29" @default.
• W2002153815 title "The REBMIX Algorithm for the Multivariate Finite Mixture Estimation" @default.
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• W2002153815 doi "https://doi.org/10.1080/03610921003725788" @default.
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