Pseudo-likelihood, explanatory power, and Bayes's theorem [Comment on: ``A likelihood paradigm for clinical trials]

DOI10.1080/15598608.2013.771546zbMath1425.62006OpenAlexW2042607750WikidataQ57826629 ScholiaQ57826629MaRDI QIDQ2320825

David R. Bickel

Publication date: 27 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15598608.2013.771546

zbMATH Keywords

clinical trialslikelihood paradigm

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Foundations and philosophical topics in statistics (62A01)

Related Items (4)

The \(p\)-value interpreted as the posterior probability of explaining the data: applications to multiple testing and to restricted parameter spaces ⋮ Self-consistent confidence sets and tests of composite hypotheses applicable to restricted parameters ⋮ Model fusion and multiple testing in the likelihood paradigm: shrinkage and evidence supporting a point null hypothesis ⋮ The sufficiency of the evidence, the relevancy of the evidence, and quantifying both with a single number

Cites Work

• Is the \(p\)-value a good measure of evidence? Asymptotic consistency criteria
• That BLUP is a good thing: The estimation of random effects. With comments and a rejoinder by the author
• A likelihood paradigm for clinical trials
• A predictive approach to measuring the strength of statistical evidence for single and multiple comparisons
• Statistical Evidence
• On the Probability of Observing Misleading Statistical Evidence
• Minimax‐Optimal Strength of Statistical Evidence for a Composite Alternative Hypothesis
• Axiomatic Development of Profile Likelihoods as the Strength of Evidence for Composite Hypotheses
• Consistent Estimates Based on Partially Consistent Observations
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