An objective non-local prior for skew-symmetric models

2026/03/09 by F. J. Rubio · 1 voice
#stat.ME

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Abstract

We propose an objective non-local prior for testing symmetry against skew-symmetric alternatives. The prior is derived through a formal construction rule by assigning a uniform distribution to a discrepancy-based measure of the shape parameter's effect. This approach avoids the need for user-specified hyperparameters and produces a weakly informative prior tailored to the skew-symmetric family. We illustrate the use of the proposed prior in the context of testing normality against skew-normal alternatives through both a simulation study and a real-data application.

Citations

• On Bayes factor functions
• On a prior based on the Wasserstein information matrix
• Tractable Bayesian variable selection: beyond normality
• Natural (non-)informative priors for skew-symmetric distributions
• On the Independence Jeffreys prior for skew--symmetric models with applications
• Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

Discussions

• New short paper forthcoming in Statistics & Probability Letters: An objective non-local prior for skew-symmetric models. arxiv.org/abs/2603.08285 This paper develops a Moment-Objective Minimum-Discrep [bsky, 7 points, 1 comments]

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