A Bayesian inference for time series via copula-based Markov chain models

DOI10.1080/03610918.2018.1529241zbMath1489.62289OpenAlexW2900497781MaRDI QIDQ5083906

Takeshi Emura, Li-Hsien Sun, Chang-Shang Lee

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1529241

zbMATH Keywords

Markov chain Monte CarloMetropolis-Hastings algorithmBayesian inferenceClayton copulanonstandardized Student's \(t\)-distribution

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Monte Carlo methods (65C05)

Related Items

Unnamed Item ⋮ Model diagnostic procedures for copula-based Markov chain models for statistical process control ⋮ Estimation under copula-based Markov normal mixture models for serially correlated data ⋮ A copula-based Markov chain model for serially dependent event times with a dependent terminal event

Uses Software

• BayesDA
• Copula.Markov

Cites Work

• Unnamed Item
• Unnamed Item
• Estimation of copula-based semiparametric time series models
• An introduction to copulas.
• Modeling stock markets' volatility using GARCH models with normal, Student's \(t\) and stable Paretian distributions
• Copulas and Markov processes
• Optimal scaling for various Metropolis-Hastings algorithms.
• Parameter change tests for ARMA-GARCH models
• Empirical evidence on Student-\(t\) log-returns of diversified world stock indices
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Markov chain Monte Carlo in conditionally Gaussian state space models
• The Effect of Improper Priors on Gibbs Sampling in Hierarchical Linear Mixed Models
• A Test for Normality of Observations and Regression Residuals
• Equation of State Calculations by Fast Computing Machines
• R routines for performing estimation and statistical process control under copula-based time series models
• Monte Carlo sampling methods using Markov chains and their applications
• Understanding Relationships Using Copulas
• CONTINUOUS INSPECTION SCHEMES
• Introducing Monte Carlo Methods with R
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)