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Open AccessArticle

Quantile-Based Estimation of the Finite Cauchy Mixture Model

1
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Department of Mathematical Sciences and Institute for Data Science, Durham University, Durham DH1 3LE, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2019, 11(9), 1186; https://doi.org/10.3390/sym11091186
Received: 22 August 2019 / Revised: 9 September 2019 / Accepted: 16 September 2019 / Published: 19 September 2019
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. View Full-Text
Keywords: cauchy distribution; mixture model; outliers; weighted quantiles; image analysis cauchy distribution; mixture model; outliers; weighted quantiles; image analysis
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MDPI and ACS Style

Kalantan, Z.I.; Einbeck, J. Quantile-Based Estimation of the Finite Cauchy Mixture Model. Symmetry 2019, 11, 1186.

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