Research Repository

Exact change point detection with improved power in small‐sample binomial sequences

Ellenberger, David and Lausen, Berthold and Friede, Tim (2021) 'Exact change point detection with improved power in small‐sample binomial sequences.' Biometrical Journal, o (3). pp. 558-574. ISSN 0323-3847

[img]
Preview
Text
bimj.201900273.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

Abstract

To detect a change in the probability of a sequence of independent binomial random variables, a variety of asymptotic and exact testing procedures have been proposed. Whenever the sample size or the event rate is small, asymptotic approximations of maximally selected test statistics have been shown to be inaccurate. Although exact methods control the type I error rate, they can be overly conservative due to the discreteness of the test statistics in these situations. We extend approaches by Worsley and Halpern to develop a test that is less discrete to increase the power. Building on ideas from binary segmentation, the proposed test utilizes unused information in the binomial sequences to add a new ordering to test statistics that are of equal value. The exact distributions are derived under side conditions that arise in hypothetical segmentation steps and do not depend on the type of test statistic used (e.g., log likelihood ratio, cumulative sum, or Fisher's exact test). Using the proposed exact segmentation procedure, we construct a change point test and prove that it controls the type‐I‐error rate at any given nominal level. Furthermore, we prove that the new test is uniformly at least as powerful as Worsley's exact test. In a Monte Carlo simulation study, the gain in power can be remarkable, especially in scenarios with small sample size. Giving a clinical database example about pin site infections and an example assessing publication bias in neuropsychiatric drug research, we demonstrate the wide‐ranging applicability of the test.

Item Type: Article
Uncontrolled Keywords: binary segmentation; change point; disorder detection; exact test; Worsley' s test
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 09 Feb 2021 11:26
Last Modified: 18 Aug 2022 12:22
URI: http://repository.essex.ac.uk/id/eprint/29732

Actions (login required)

View Item View Item