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A moment-distance hybrid method for estimating a mixture of two symmetric densities
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Patrik Rydén)
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Patrik Rydén)
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Patrik Rydén)
2018 (English)In: Modern Stochastics: Theory and Applications, ISSN 2351-6054, Vol. 5, no 1, p. 1-36Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 5, no 1, p. 1-36
Keywords [en]
inference for mixtures, method of moments, minimum distance, model-based clustering
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-144644DOI: 10.15559/17-VMSTA93ISI: 000434875200001OAI: oai:DiVA.org:umu-144644DiVA, id: diva2:1181475
Funder
Swedish Research Council, 340-2013-5185Available from: 2018-02-08 Created: 2018-02-08 Last updated: 2018-09-19Bibliographically approved

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Källberg, DavidBelyaev, YuriRydén, Patrik
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