Statistical Science

Nonparametric Bayesian Clay for Robust Decision Bricks

Christian P. Robert and Judith Rousseau

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Abstract

This note discusses Watson and Holmes [Statist. Sci. (2016) 31 465–489] and their proposals towards more robust Bayesian decisions. While we acknowledge and commend the authors for setting new and all-encompassing principles of Bayesian robustness, and while we appreciate the strong anchoring of these within a decision-theoretic framework, we remain uncertain as to what extent such principles can be applied outside binary decisions. We also wonder at the ultimate relevance of Kullback–Leibler neighbourhoods into characterising robustness and we instead favour extensions along nonparametric axes.

Article information

Source
Statist. Sci. Volume 31, Number 4 (2016), 506-510.

Dates
First available in Project Euclid: 19 January 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1484816577

Digital Object Identifier
doi:10.1214/16-STS567

Keywords
Decision-theory Gamma-minimaxity misspecification prior selection robust methodology

Citation

Robert, Christian P.; Rousseau, Judith. Nonparametric Bayesian Clay for Robust Decision Bricks. Statist. Sci. 31 (2016), no. 4, 506--510. doi:10.1214/16-STS567. https://projecteuclid.org/euclid.ss/1484816577


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See also