Article
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A Parametric Bayesian Approach in Density Ratio Estimation
Version 1
: Received: 3 March 2019 / Approved: 4 March 2019 / Online: 4 March 2019 (09:43:12 CET)
How to cite: Sadeghkhani, A.; Peng, Y.; Lin, D. A Parametric Bayesian Approach in Density Ratio Estimation. Preprints 2019, 2019030025 (doi: 10.20944/preprints201903.0025.v1). Sadeghkhani, A.; Peng, Y.; Lin, D. A Parametric Bayesian Approach in Density Ratio Estimation. Preprints 2019, 2019030025 (doi: 10.20944/preprints201903.0025.v1).
Abstract
This paper considers 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 to find robust M-estimators. We propose two different types of Bayesian density ratio estimators and compare their performance in terms of Bayesian risk function with themselves as well as the usual plug-in density ratio estimators. Some applications such as classification and divergence function estimation are addressed.
Keywords
Bayes estimator, Bregman divergence, Density ratio, Exponential family, Log--Huber loss.
Subject
MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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