caa a22 4500 606196579 CHVBK 20210128100925.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s10260-015-0299-6 doi (NATIONALLICENCE)springer-10.1007/s10260-015-0299-6 Bickel David Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, 451 Smyth Road, K1H 8M5, Ottawa, ON, Canada aut Blending Bayesian and frequentist methods according to the precision of prior information with applications to hypothesis testing [Elektronische Daten] [David Bickel] The proposed minimax procedure blends strict Bayesian methods with p values and confidence intervals or with default-prior methods. Two applications to hypothesis testing bring some implications to light. First, the blended probability that a point null hypothesis is true is equal to the p value or a lower bound of an unknown posterior probability, whichever is greater. As a result, the p value is reported instead of any posterior probability in the case of complete prior ignorance but is ignored in the case of a fully known prior. In the case of partial knowledge about the prior, the possible posterior probability that is closest to the p value is used for inference. The second application provides guidance on the choice of methods used for small numbers of tests as opposed to those appropriate for large numbers. Whereas statisticians tend to prefer a multiple comparison procedure that adjusts each p value for small numbers of tests, large numbers instead lead many to estimate the local false discovery rate (LFDR), a posterior probability of hypothesis truth. Each blended probability reduces to the LFDR estimate if it can be estimated with sufficient accuracy or to the adjusted p value otherwise. Springer-Verlag Berlin Heidelberg, 2015 Confidence distribution nationallicence Fiducial inference nationallicence Imprecise probability nationallicence Maximum entropy nationallicence Multiple hypothesis testing nationallicence Multiple comparison procedure nationallicence Robust Bayesian analysis nationallicence Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 523-546 1618-2510 24:4<523 2015 24 10260 https://doi.org/10.1007/s10260-015-0299-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-015-0299-6 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Bickel David Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, 451 Smyth Road, K1H 8M5, Ottawa, ON, Canada aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 523-546 1618-2510 24:4<523 2015 24 10260