The tenets of quantile-based inference in Bayesian models

Dmytro Perepolkin, Benjamin Goodrich and Ullrika Sahlin

Computational Statistics & Data Analysis, 2023, vol. 187, issue C

Abstract: Bayesian inference can be extended to probability distributions defined in terms of their inverse distribution function, i.e. their quantile function. This applies to both prior and likelihood. Quantile-based likelihood is useful in models with sampling distributions which lack an explicit probability density function. Quantile-based prior allows for flexible distributions to express expert knowledge. The principle of quantile-based Bayesian inference is demonstrated in the univariate setting with a Govindarajulu likelihood, as well as in a parametric quantile regression, where the error term is described by a quantile function of a Flattened Skew-Logistic distribution.

Keywords: Bayesian analysis; Quantile functions; Quantile-based inference; Parametric quantile regression (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001068

DOI: 10.1016/j.csda.2023.107795

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