A general Bayesian model for heteroskedastic data with fully conjugate full-conditional distributions

DOI10.1080/00949655.2021.1925279OpenAlexW3165407714MaRDI QIDQ3389666

Skye A. Wills, Paul A. Parker, Scott H. Holan

Publication date: 23 March 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.13636

zbMATH Keywords

spatialGibbs samplingvolatilitymixed modelsecho state networkdeep learningmultivariate log-gamma

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Stan
• FRK
• vbdcast

Cites Work

• The Pricing of Options and Corporate Liabilities
• Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors
• Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study
• Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion)
• Generalized autoregressive conditional heteroscedasticity
• Spatial stochastic volatility for lattice data
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• The Bayesian Lasso
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Inference in Arch and Garch Models with Heavy-Tailed Errors
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