Objective Bayesian analysis for the Student-\(t\) linear regression
From MaRDI portal
Publication:2057376
DOI10.1214/20-BA1198zbMath1475.62206OpenAlexW3010902555MaRDI QIDQ2057376
Dongchu Sun, Lei He, Dao-Jiang He
Publication date: 6 December 2021
Published in: Bayesian Analysis (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ba/1583805750
Related Items
A simulation study to compare reference and other priors in the case of a standard univariate Student t-distribution, Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects, Online inference with multi-modal likelihood functions, A Bayesian regression model for the non-standardized \(t\) distribution with location, scale and degrees of freedom parameters, Theoretical properties of Bayesian Student-\(t\) linear regression
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Posterior property of Student-\(t\) linear regression model using objective priors
- Stochastic volatility in mean models with heavy-tailed distributions
- Objective prior for the number of degrees of freedom of a \(t\) distribution
- Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
- Estimation of error variance in linear regression models with errors having multivariate Student-\(t\) distribution with unknown degrees of freedom
- The formal definition of reference priors
- Inconsistency of estimate of the degree of freedom of multivariate Student-\(t\) disturbances in linear regression models
- Reference priors for exponential families with increasing dimension
- Objective Bayesian analysis for the Student-t regression model
- Ordered group reference priors with application to the multinomial problem
- Bayesian and Non-Bayesian Analysis of the Regression Model with Multivariate Student-t Error Terms
- Multivariate Student-t regression models: Pitfalls and inference
- 'Objective Bayesian analysis for the Student-t regression model'