Journal
COMPUTATIONAL STATISTICS
Volume 28, Issue 4, Pages 1571-1597Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-012-0367-4
Keywords
Finite mixtures of densities; Pearson system; EM algorithm; Bump hunting; Partitional clustering methods
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This paper addresses the problem of estimating a density, with either a compact support or a support bounded at only one end, exploiting a general and natural form of a finite mixture of distributions. Due to the importance of the concept of multimodality in the mixture framework, unimodal beta and gamma densities are used as mixture components, leading to a flexible modeling approach. Accordingly, a mode-based parameterization of the components is provided. A partitional clustering method, named -bumps, is also proposed; it is used as an ad hoc initialization strategy in the EM algorithm to obtain the maximum likelihood estimation of the mixture parameters. The performance of the -bumps algorithm as an initialization tool, in comparison to other common initialization strategies, is evaluated through some simulation experiments. Finally, two real applications are presented.
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