Allard A. Hendriksen, Rianne de Heide, Peter Grünwald
Published in Bayesian Analysis on 01 September 2021.
It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings that various authors give to this claim: (1) stopping rule independence, (2) posterior calibration and (3) (semi-) frequentist robustness to optional stopping. We then prove theorems to the effect that these claims do indeed hold in a general measure-theoretic setting. For claims of type (2) and (3), such results are new. By allowing for non-integrable measures based on improper priors, we obtain particularly strong results for the practically important case of models with nuisance parameters satisfying a group invariance (such as location or scale). We also discuss the practical relevance of (1)–(3), and conclude that whether Bayes factor methods actually perform well under optional stopping crucially depends on details of models, priors and the goal of the analysis.
We were invited to write a post related to our paper on the blog of the Young Statisticians Europe (YSE). It was written by Rianne.
@Article{hendriksen-2020-option-stopp,
author = {Hendriksen, Allard and de Heide, Rianne and Grünwald,
Peter},
title = {Optional Stopping With {Bayes} Factors: a Categorization
and Extension of Folklore Results, With an Application To
Invariant Situations},
journal = {Bayesian Analysis},
volume = 16,
number = 3,
pages = {961 -- 989},
year = 2021,
doi = {10.1214/20-BA1234},
URL = {https://doi.org/10.1214/20-BA1234},
keywords = {Bayes factors, Bayesian testing, group invariance, optional
stopping, right Haar prior},
publisher = {International Society for Bayesian Analysis},
}