Controlling the error probabilities of model selection information criteria using bootstrapping

DOI10.1080/02664763.2019.1701636OpenAlexW2994876975WikidataQ126544566 ScholiaQ126544566MaRDI QIDQ5861435

Michael Cullan, Scott Lidgard, Beckett Sterner

Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2019.1701636

zbMATH Keywords

bootstraphypothesis testingnon-nested modelserror statisticsNeyman-Pearson classification

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (1)

Editorial to special issue V WCDANM 2018

Uses Software

• R

Cites Work

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• Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
• The Model Confidence Set
• Akaike-type criteria and the reliability of inference: model selection versus statistical model specification
• To explain or to predict?
• Bootstrap methods: another look at the jackknife
• Assessing model mimicry using the parametric bootstrap.
• Weak convergence of smoothed and nonsmoothed bootstrap quantile estimates
• Performance Measures for Neyman–Pearson Classification
• The bootstrap: To smooth or not to smooth?
• Model Selection and Multimodel Inference
• IX. On the problem of the most efficient tests of statistical hypotheses
• Estimation and Accuracy After Model Selection
• Bridging AIC and BIC: A New Criterion for Autoregression
• Models and Statistical Inference: The Controversy between Fisher and Neyman–Pearson
• Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
• Best-subset model selection based on multitudinal assessments of likelihood improvements

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