Adaptative significance levels using optimal decision rules: balancing by weighting the error probabilities

DOI10.1214/14-BJPS257zbMath1381.62015WikidataQ63362816 ScholiaQ63362816MaRDI QIDQ288004

Luís Raúl Pericchi, Carlos Alberto de Bragança Pereira

Publication date: 23 May 2016

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bjps/1453211803

zbMATH Keywords

hypothesis testingerrors type I and type IIsignificance levelsweighting error probabilities

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Foundations and philosophical topics in statistics (62A01) General considerations in statistical decision theory (62C05)

Related Items (4)

Balancing producer and consumer risks in optimal attribute testing: a unified Bayesian/frequentist design ⋮ History and nature of the Jeffreys-Lindley paradox ⋮ New Bayesian approaches to equivalence testing ⋮ Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels

Cites Work

• The interplay of Bayesian and frequentist analysis
• Changing statistical significance with the amount of information: the adaptive \(\alpha\) significance level
• The Intrinsic Bayes Factor for Model Selection and Prediction
• A STATISTICAL PARADOX
• Inference for a Bernoulli Process (A Bayesian View)
• Bayes Factors
• More on Concepts of Statistical Evidence
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