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Calibrated Bayes factors under flexible priors

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  • Dan J. Spitzner

    (University of Virginia)

Abstract

This article develops and explores a robust Bayes factor derived from a calibration technique that makes it particularly compatible with elicited prior knowledge. Building on previous explorations, the particular robust Bayes factor, dubbed a neutral-data comparison, is adapted for broad comparisons with existing robust Bayes factors, such as the fractional and intrinsic Bayes factors, in configurations defined by informative priors. The calibration technique is furthermore developed for use with flexible parametric priors—that is, mixture prior distributions with components that may be symmetric or skewed—, and demonstrated in an example context from forensic science. Throughout the exploration, the neutral-data comparison is shown to exhibit desirable sensitivity properties, and to show promise for adaptation to elaborate data-analysis scenarios.

Suggested Citation

  • Dan J. Spitzner, 2023. "Calibrated Bayes factors under flexible priors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 733-767, September.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:3:d:10.1007_s10260-023-00683-4
    DOI: 10.1007/s10260-023-00683-4
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    References listed on IDEAS

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    1. James Berger & Elías Moreno & Luis Pericchi & M. Bayarri & José Bernardo & Juan Cano & Julián Horra & Jacinto Martín & David Ríos-Insúa & Bruno Betrò & A. Dasgupta & Paul Gustafson & Larry Wasserman &, 1994. "An overview of robust Bayesian analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 3(1), pages 5-124, June.
    2. C. G. G. Aitken & D. Lucy, 2004. "Evaluation of trace evidence in the form of multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 109-122, January.
    3. C. G. G. Aitken & D. Lucy, 2004. "Corrigendum: Evaluation of trace evidence in the form of multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(4), pages 665-666, November.
    4. C. M. Carvalho & J. G. Scott, 2009. "Objective Bayesian model selection in Gaussian graphical models," Biometrika, Biometrika Trust, vol. 96(3), pages 497-512.
    5. Fulvio De Santis & Fulvio Spezzaferri, 1999. "Methods for Default and Robust Bayesian Model Comparison: the Fractional Bayes Factor Approach," International Statistical Review, International Statistical Institute, vol. 67(3), pages 267-286, December.
    6. Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
    7. Guido Consonni & Luca La Rocca & Stefano Peluso, 2017. "Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 741-764, September.
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