Quantile-Based Estimation of the Finite Cauchy Mixture Model

Kalantan, Zakiah I.; Einbeck, Jochen

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
• Electronic ISSN: 2073-8994
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 9
• Article Number: 1186
• DOI: https://doi.org/10.3390/sym11091186
• Public URL: https://durham-repository.worktribe.com/output/1290348
• Related Public URLs: https://www.researchgate.net/publication/335940768_Quantile-Based_Estimation_of_the_Finite_Cauchy_Mixture_Model