A Bayesian Approach to Successive Comparisons

Autor:	Mohammad Reza Meshkani, Ali Aghamohammadi, Mohsen Mohammadzadeh
Rok vydání:	2021
Předmět:	False discovery rate Closed testing procedure Bayes' theorem Mathematical optimization Resampling Multiple comparisons problem Word error rate Holm–Bonferroni method Bayesian inference Mathematics
Zdroj:	Journal of Data Science. 8:541-553
ISSN:	1683-8602 1680-743X
DOI:	10.6339/jds.2010.08(4).630
Popis:	The present article discusses and compares multiple testing pro- cedures (MTPs) for controlling the family wise error rate. Machekano and Hubbard (2006) have proposed empirical Bayes approach that is a resam- pling based multiple testing procedure asymptotically controlling the fam- ilywise error rate. In this paper we provide some additional work on their procedure, and we develop resampling based step-down procedure asymptot- ically controlling the familywise error rate for testing the families of one-sided hypotheses. We apply these procedures for making successive comparisons between the treatment effects under a simple-order assumption. For exam- ple, the treatment means may be a sequences of increasing dose levels of a drug. Using simulations, we demonstrate that the proposed step-down pro- cedure is less conservative than the Machekano and Hubbard's procedure. The application of the procedure is illustrated with an example.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c3f63c1079bea4812b3bcb420bd79965 https://doi.org/10.6339/jds.2010.08(4).630 Zobrazit plný text záznamu