Calibrated Bayes factors under flexible priors

Dan J. Spitzner ()
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Dan J. Spitzner: University of Virginia

Statistical Methods & Applications, 2023, vol. 32, issue 3, No 2, 733-767

Abstract: 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.

Keywords: Robust bayes factor; Elicitation; Fractional bayes factor; Intrinsic bayes factor; Mixture prior; Forensic science (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-023-00683-4

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