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The tenets of quant...
The tenets of quantile-based inference in Bayesian models
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- Perepolkin, Dmytro (author)
- Lund University,Lunds universitet,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Centre for Environmental and Climate Science (CEC),Faculty of Science
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- Goodrich, Benjamin (author)
- Columbia University
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- Sahlin, Ullrika (author)
- Lund University,Lunds universitet,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Centre for Environmental and Climate Science (CEC),Faculty of Science
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(creator_code:org_t)
- 2023
- 2023
- English 15 s.
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In: Computational Statistics and Data Analysis. - 0167-9473. ; 187
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Abstract
Subject headings
Close
- 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.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- Bayesian analysis
- Parametric quantile regression
- Quantile functions
- Quantile-based inference
Publication and Content Type
- art (subject category)
- ref (subject category)
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