Objective Bayesian multiple comparisons for normal variances

DOI10.1080/00949655.2016.1235168OpenAlexW2523568982MaRDI QIDQ5106827

Woo Dong Lee, Yongku Kim, Sang Gil Kang

Publication date: 22 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2016.1235168

zbMATH Keywords

Bayes factormultiple comparisonsreference priorintrinsic priorfractional prior

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Default Bayesian analysis of the Behrens-Fisher problem
• Estimating the dimension of a model
• Properties of intrinsic and fractional Bayes factors
• Bayesian perspectives on multiple comparisons.
• A default Bayesian test for the number of components in a mixture
• Consistency of Bayes factors for intrinsic priors in normal linear models
• Principles of combinatorics
• The Intrinsic Bayes Factor for Model Selection and Prediction
• Bayesian Multiple Comparisons Using Dirichlet Process Priors
• An Intrinsic Limiting Procedure for Model Selection and Hypotheses Testing
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• Methods for Default and Robust Bayesian Model Comparison: The Fractional Bayes Factor Approach
• A Bayesian Approach to Multiple Comparisons
• Objective Bayesian Variable Selection
• Some multiple comparison procedures for variances from non-normal populations.

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