The tenets of quantile-based inference in Bayesian models
(2023) In Computational Statistics and Data Analysis 187.- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ae7bfd65-2f17-4bd6-8e85-6ee952934cf0
- author
- Perepolkin, Dmytro LU ; Goodrich, Benjamin and Sahlin, Ullrika LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayesian analysis, Parametric quantile regression, Quantile functions, Quantile-based inference
- in
- Computational Statistics and Data Analysis
- volume
- 187
- article number
- 107795
- pages
- 15 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85162099398
- ISSN
- 0167-9473
- DOI
- 10.1016/j.csda.2023.107795
- language
- English
- LU publication?
- yes
- id
- ae7bfd65-2f17-4bd6-8e85-6ee952934cf0
- date added to LUP
- 2023-08-23 15:17:06
- date last changed
- 2023-12-07 07:05:32
@article{ae7bfd65-2f17-4bd6-8e85-6ee952934cf0, abstract = {{<p>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.</p>}}, author = {{Perepolkin, Dmytro and Goodrich, Benjamin and Sahlin, Ullrika}}, issn = {{0167-9473}}, keywords = {{Bayesian analysis; Parametric quantile regression; Quantile functions; Quantile-based inference}}, language = {{eng}}, publisher = {{Elsevier}}, series = {{Computational Statistics and Data Analysis}}, title = {{The tenets of quantile-based inference in Bayesian models}}, url = {{http://dx.doi.org/10.1016/j.csda.2023.107795}}, doi = {{10.1016/j.csda.2023.107795}}, volume = {{187}}, year = {{2023}}, }