title: Optimal Hypothesis Testing: From Semi to Fully Bayes Factors.
abstract: We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis. We demonstrate that our approach can be easily applied to practical studies, because execution of the tests does not require deriving of asymptotical analytical solutions regarding the type I error. However, when the proposed method is utilized, the classical significance level of tests can be controlled.
authors.affiliation: Department of Biostatistics, The New York State University at Buffalo, Buffalo, NY 14214, USA avexler@buffalo.edu.
authors.forename: Albert
authors.initials: A
authors.lastname: Vexler
authors.forename: Chengqing
authors.initials: C
authors.lastname: Wu
authors.forename: Kai Fun
authors.initials: KF
authors.lastname: Yu