Online Program Home
My Program

Abstract Details

Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #306625
Title: Bayesian Agnostic Multiple-Hypotheses Test with Decision-Errors Control
Author(s): Marcio Augusto Diniz* and Melaine Oliveira Couch and Zahra Razaee and Andre Rogatko
Companies: Cedars Sinai Medical Center and Florida State University and Cedars-Sinai Medical Center and Cedars-Sinai Medical Center
Keywords: agnostic test; decision errors; Bayesian; multiple hypotheses

Any decision process is associated with the possibility of committing errors. When two hypotheses are considered, frequentist methods introduced by R.A. Fisher and extended by Neyman-Pearson control the maximum probability decision error of the null hypothesis to be while minimizing the probability of the type II error. On the other hand, Bayesian methods minimize the linear combination of decision error probabilities of both hypotheses. However, these errors are typically unmeasured and uncontrolled. Scientists wish to control both errors when testing hypotheses. A known strategy is through sequential sampling, even though the possibility of continuous sampling until a decision can be reached is uncommon in practice. Usually, sample sizes are fixed. To control both decision errors something got to give: one needs to include a third possibility, that neither of the two hypotheses can be accepted. We remain undecided, or agnostic. We present a Bayesian agnostic test that controls the average predictive probability of decision errors that is also suitable for multiple hypothesis testing and, by controlling all the decision-error probabilities, favors reproducibility.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program