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Quantile-based estimation of the finite cauchy mixture model.

Kalantan, Zakiah I. and Einbeck, Jochen (2019) 'Quantile-based estimation of the finite cauchy mixture model.', Symmetry, 11 (9). p. 1186.

Abstract

Heterogeneity and outliers are two aspects which add considerable complexity to the analysis of data. The Cauchy mixture model is an attractive device to deal with both issues simultaneously. This paper develops an Expectation-Maximization-type algorithm to estimate the Cauchy mixture parameters. The main ingredient of the algorithm are appropriately weighted component-wise quantiles which can be efficiently computed. The effectiveness of the method is demonstrated through a simulation study, and the techniques are illustrated by real data from the fields of psychology, engineering and computer vision.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.3390/sym11091186
Publisher statement:© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Date accepted:16 September 2019
Date deposited:04 October 2019
Date of first online publication:19 September 2019
Date first made open access:04 October 2019

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