Bayesian Analysis

Testing Un-Separated Hypotheses by Estimating a Distance

Jean-Bernard Salomond

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Abstract

In this paper we propose a Bayesian answer to testing problems when the hypotheses are not well separated. The idea of the method is to study the posterior distribution of a discrepancy measure between the parameter and the model we want to test for. This is shown to be equivalent to a modification of the testing loss. An advantage of this approach is that it can easily be adapted to complex hypotheses testing which are in general difficult to test for. Asymptotic properties of the test can be derived from the asymptotic behaviour of the posterior distribution of the discrepancy measure, and gives insight on possible calibrations. In addition one can derive separation rates for testing, which ensure the asymptotic frequentist optimality of our procedures.

Article information

Source
Bayesian Anal. (2017), 24 pages.

Dates
First available in Project Euclid: 23 June 2017

Permanent link to this document
https://projecteuclid.org/euclid.ba/1498204951

Digital Object Identifier
doi:10.1214/17-BA1059

Keywords
hypothesis testing Bayesian inference asymptotic properties of tests nonparametric inference goodness-of-fit monotonicity

Rights
Creative Commons Attribution 4.0 International License.

Citation

Salomond, Jean-Bernard. Testing Un-Separated Hypotheses by Estimating a Distance. Bayesian Anal., advance publication, 23 June 2017. doi:10.1214/17-BA1059. https://projecteuclid.org/euclid.ba/1498204951


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