A Parametric Bayesian Approach in Density Ratio Estimation

Abdolnasser Sadeghkhani, Yingwei Peng and Chunfang Devon Lin
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Abdolnasser Sadeghkhani: Department of Mathematics & Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada
Yingwei Peng: Departments of Public Health Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
Chunfang Devon Lin: Department of Mathematics & Statistics, Queen’s University, Kingston, ON K7L 3N6, Canada

Stats, 2019, vol. 2, issue 2, 1-13

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.

Keywords: Bayes estimator; Bregman divergence; density ratio; exponential family; log–Huber loss (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2019
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