Quantile-Based Estimation of the Finite Cauchy Mixture Model

Kalantan, Zakiah I.; Einbeck, Jochen

doi:10.3390/sym11091186

Quantile-Based Estimation of the Finite Cauchy Mixture Model

Kalantan, Zakiah I.; Einbeck, Jochen

Authors

Zakiah I. Kalantan

Professor Jochen Einbeck jochen.einbeck@durham.ac.uk
Professor

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.

Citation

Kalantan, Z. I., & Einbeck, J. (2019). Quantile-Based Estimation of the Finite Cauchy Mixture Model. Symmetry, 11(9), Article 1186. https://doi.org/10.3390/sym11091186

Journal Article Type	Article
Acceptance Date	Sep 16, 2019
Online Publication Date	Sep 19, 2019
Publication Date	Sep 19, 2019
Deposit Date	Sep 27, 2019
Publicly Available Date	Oct 4, 2019
Journal	Symmetry
Publisher	MDPI
Peer Reviewed	Peer Reviewed
Volume	11
Issue	9
Article Number	1186
DOI	https://doi.org/10.3390/sym11091186
Related Public URLs	https://www.researchgate.net/publication/335940768_Quantile-Based_Estimation_of_the_Finite_Cauchy_Mixture_Model

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