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P‐value calibration in multiple hypotheses testing
Statistics in medicine, 2017-08, Vol.36 (18), p.2875-2886
Cabras, Stefano
Castellanos, Maria Eugenia
2017
Details
Autor(en) / Beteiligte
Cabras, Stefano
Castellanos, Maria Eugenia
Titel
P‐value calibration in multiple hypotheses testing
Ist Teil von
Statistics in medicine, 2017-08, Vol.36 (18), p.2875-2886
Ort / Verlag
England: Wiley Subscription Services, Inc
Erscheinungsjahr
2017
Link zum Volltext
Quelle
Wiley Online Library (Online service)
Beschreibungen/Notizen
As p‐values are the most common measures of evidence against a hypothesis, their calibration with respect to null hypothesis conditional probability is important in order to match frequentist unconditional inference with the Bayesian ones. The Selke, Bayarri and Berger calibration is one of the most popular attempts to obtain such a calibration. This relies on the theoretical sampling null distribution of p‐values, which is the well‐known Uniform(0,1), but arising only for specific sampling models. We generalize this calibration by considering a sampling null distribution estimated from the data. It is possible to obtain such an empirical null distribution, for instance, in the context of multiple testing in which many p‐values come from the null model. Such a context is purely instrumental for the purposes of p‐value calibration, and multiple testing still needs to be considered with appropriate techniques. The new calibration proposed here still remains a simple analytic formula like the original one under the Uniform(0,1) and basically provides a stronger interpretation framework for the widely used p‐value. Copyright © 2017 John Wiley & Sons, Ltd.
Sprache
Englisch
Identifikatoren
ISSN: 0277-6715
eISSN: 1097-0258
DOI: 10.1002/sim.7330
Titel-ID: cdi_proquest_miscellaneous_1897804354
Format
–
Schlagworte
Animals
,
Bayes factor lower bound
,
Bayes Theorem
,
Biostatistics
,
Calibration
,
Cattle
,
Humans
,
Hypothesis testing
,
Macrophages - metabolism
,
Male
,
Medical statistics
,
Models, Statistical
,
non‐parametric Bayes
,
objective Bayes
,
Oligonucleotide Array Sequence Analysis - statistics & numerical data
,
Probability
,
Prostatic Neoplasms - genetics
,
Sequence Analysis, RNA - statistics & numerical data
,
significance testing
,
Statistics, Nonparametric
,
Tuberculosis, Bovine - genetics
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