Measuring Bayesian Robustness Using Rényi Divergence

Luai Al-Labadi, Forough Fazeli Asl and Ce Wang
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Luai Al-Labadi: Department of Mathematical & Computational Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
Forough Fazeli Asl: Department of Mathematical Sciences, Isfahan University of Technology, Isfahan 84156-83111, Iran
Ce Wang: Department of Mathematical & Computational Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada

Stats, 2021, vol. 4, issue 2, 1-18

Abstract: This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two different classes of priors in the neighborhood of the elicited prior are considered. The first one is the well-known ϵ -contaminated class, while the second one is the geometric mixing class. The proposed measure of robustness is based on computing the curvature of Rényi divergence between posterior distributions. Examples are used to illustrate the results by using simulated and real data sets.

Keywords: Bayesian robustness; ϵ-contamination; geometric contamination; Rényi divergence (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
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