Objective prior for the number of degrees of freedom of a \(t\) distribution

DOI10.1214/13-BA854zbMath1327.62168MaRDI QIDQ899016

Cristiano Villa, Stephen G. Walker

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1393251776

zbMATH Keywords

Kullback-Leibler divergencelinear regressionobjective priorrobust analysis\(t\) distributionfinancial returnself-information loss function

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Bayesian inference (62F15) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (10)

Posterior property of Student-\(t\) linear regression model using objective priors ⋮ Bayesian linear regression models with flexible error distributions ⋮ A simulation study to compare reference and other priors in the case of a standard univariate Student t-distribution ⋮ Prior distributions for objective Bayesian analysis ⋮ An Objective Bayesian Criterion to Determine Model Prior Probabilities ⋮ Objective priors for the number of degrees of freedom of a multivariate \(t\) distribution and the \(t\)-copula ⋮ Bayesian modelling of skewness and kurtosis with two-piece scale and shape distributions ⋮ Inducing high spatial correlation with randomly edge-weighted neighborhood graphs ⋮ Objective Bayesian analysis for the Student-\(t\) linear regression ⋮ A Bayesian regression model for the non-standardized \(t\) distribution with location, scale and degrees of freedom parameters

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