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. Preprints2019, 2019030025. https://doi.org/10.20944/preprints201903.0025.v1
Sadeghkhani, A.; Peng, Y.; Lin, D. A Parametric Bayesian Approach in Density Ratio Estimation. Preprints 2019, 2019030025. https://doi.org/10.20944/preprints201903.0025.v1
Sadeghkhani, A.; Peng, Y.; Lin, D. A Parametric Bayesian Approach in Density Ratio Estimation. Preprints2019, 2019030025. https://doi.org/10.20944/preprints201903.0025.v1
APA Style
Sadeghkhani, A., Peng, Y., & Lin, D. (2019). A Parametric Bayesian Approach in Density Ratio Estimation. Preprints. https://doi.org/10.20944/preprints201903.0025.v1
Chicago/Turabian Style
Sadeghkhani, A., Yingwei Peng and Devon Lin. 2019 "A Parametric Bayesian Approach in Density Ratio Estimation" Preprints. https://doi.org/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
Computer Science and Mathematics, 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.
