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Open AccessArticle

A Parametric Bayesian Approach in Density Ratio Estimation

1
Department of Mathematics & Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada
2
Departments of Public Health Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
3
Department of Mathematics & Statistics, Queen’s University, Kingston, ON K7L 3N6, Canada
*
Author to whom correspondence should be addressed.
Stats 2019, 2(2), 189-201; https://doi.org/10.3390/stats2020014
Received: 7 March 2019 / Revised: 25 March 2019 / Accepted: 26 March 2019 / Published: 30 March 2019
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Abstract

This paper is concerned with estimating the ratio of two distributions with different parameters and common supports. We consider a Bayesian approach based on the log–Huber loss function, which is resistant to outliers and useful for finding robust M-estimators. We propose two different types of Bayesian density ratio estimators and compare their performance in terms of frequentist risk function. Some applications, such as classification and divergence function estimation, are addressed. View Full-Text
Keywords: Bayes estimator; Bregman divergence; density ratio; exponential family; log–Huber loss Bayes estimator; Bregman divergence; density ratio; exponential family; log–Huber loss
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Sadeghkhani, A.; Peng, Y.; Lin, C.D. A Parametric Bayesian Approach in Density Ratio Estimation. Stats 2019, 2, 189-201.

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