Dmytro Perepolkin
Doctoral student
The tenets of quantile-based inference in Bayesian models
Author
Summary, in English
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.
Department/s
- Centre for Environmental and Climate Science (CEC)
- BECC: Biodiversity and Ecosystem services in a Changing Climate
- MERGE: ModElling the Regional and Global Earth system
Publishing year
2023
Language
English
Publication/Series
Computational Statistics and Data Analysis
Volume
187
Document type
Journal article
Publisher
Elsevier
Topic
- Probability Theory and Statistics
Keywords
- Bayesian analysis
- Parametric quantile regression
- Quantile functions
- Quantile-based inference
Status
Published
ISBN/ISSN/Other
- ISSN: 0167-9473