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Modern Stochastics: Theory and Applications


A moment-distance hybrid method for estimating a mixture of two symmetric densities
Volume 5, Issue 1 (2018), pp. 1–36
Pub. online: 18 January 2018      Type: Research Article      Open accessOpen Access
Received
4 October 2017
Revised
13 December 2017
Accepted
18 December 2017
Published
18 January 2018

Abstract

In clustering of high-dimensional data a variable selection is commonly applied to obtain an accurate grouping of the samples. For two-class problems this selection may be carried out by fitting a mixture distribution to each variable. We propose a hybrid method for estimating a parametric mixture of two symmetric densities. The estimator combines the method of moments with the minimum distance approach. An evaluation study including both extensive simulations and gene expression data from acute leukemia patients shows that the hybrid method outperforms a maximum-likelihood estimator in model-based clustering. The hybrid estimator is flexible and performs well also under imprecise model assumptions, suggesting that it is robust and suited for real problems.

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Copyright
© 2018 The Author(s). Published by VTeX
Open access article under the CC BY license.

Keywords
Inference for mixtures method of moments minimum distance model-based clustering

MSC2010
62F07 62F10 62F35 62P10 92D10

Funding
This work was supported by grants from the Swedish Research Council (P.R.), Dnr 340-2013-5185 (P.R.), the Kempe Foundations (D.K., P.R.), Dnr JCK-1315, and the Faculty of Science and Technology, Umeå University (P.R.).

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