Estimation under copula-based Markov normal mixture models for serially correlated data

DOI10.1080/03610918.2019.1652318OpenAlexW2967502806WikidataQ127368067 ScholiaQ127368067MaRDI QIDQ5086400

Weicheng Lin, Takeshi Emura, Li-Hsien Sun

Publication date: 5 July 2022

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

Full work available at URL: https://doi.org/10.1080/03610918.2019.1652318

zbMATH Keywords

copulaMarkov modelNewton-Raphson algorithmnormal mixture distributionlog return

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Unnamed Item, A copula-based Markov chain model for serially dependent event times with a dependent terminal event

Uses Software

• Copula.Markov

Cites Work

• Unnamed Item
• Estimation of copula-based semiparametric time series models
• Root selection in normal mixture 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
• Empirical evidence on Student-\(t\) log-returns of diversified world stock indices
• Directional dependence via Gaussian copula beta regression model with asymmetric GARCH marginals
• A Test for Normality of Observations and Regression Residuals
• Model diagnostic procedures for copula-based Markov chain models for statistical process control
• A Bayesian inference for time series via copula-based Markov chain models
• Control charts of mean and variance using copula Markov SPC and conditional distribution by copula
• The Lindeberg-Levy Theorem for Martingales
• R routines for performing estimation and statistical process control under copula-based time series models
• Understanding Relationships Using Copulas