Finite mixtures of unimodal beta and gamma densities and the $$k$$ -bumps algorithm

Luca Bagnato () and Antonio Punzo

Computational Statistics, 2013, vol. 28, issue 4, 1597 pages

Abstract: 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 $$k$$ -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 $$k$$ -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. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Finite mixtures of densities; Pearson system; EM algorithm; Bump hunting; Partitional clustering methods (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:4:p:1571-1597

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DOI: 10.1007/s00180-012-0367-4

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