Approximate Bayesian model selection with the deviance statistic

DOI10.1214/14-STS510zbMath1332.62094arXiv1308.6780MaRDI QIDQ254453

Daniel Sabanés Bové, Leonhard Held, Isaac Gravestock

Publication date: 8 March 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1308.6780

zbMATH Keywords

model selectionBayes factorshrinkagegeneralized linear modeldeviance\(g\)-prior

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Censored data models (62N01) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

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Cites Work

• Unnamed Item
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• Bayesian fractional polynomials
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• Criteria for Bayesian model choice with application to variable selection
• Practical variable selection for generalized additive models
• Clinical prediction models. A practical approach to development, validation, and updating.
• Optimal predictive model selection.
• The risk inflation criterion for multiple regression
• Empirical Bayes vs. fully Bayes variable selection
• Hyper-\(g\) priors for generalized linear models
• Calibration and empirical Bayes variable selection
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• A STATISTICAL PARADOX
• Two further applications of a model for binary regression
• Model Selection and Model Averaging
• Bayesian Model Selection using Test Statistics
• Mixtures of g Priors for Bayesian Variable Selection
• On Prior Distributions for Binary Trials
• Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
• Frequentist Model Average Estimators
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• Calibration ofρValues for Testing Precise Null Hypotheses
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Bayes Factors Based on Test Statistics
• Benchmark priors for Bayesian model averaging.