Copula.Markov-package {Copula.Markov}R Documentation

Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series

Description

Copulas are applied to model a Markov dependence for serially correlated time series. The Clayton and Joe copulas are available to specify the dependence structure. The normal and binomial distributions are available for the marginal model. Maximum likelihood estimation is implmented for estimating parameters, and a Shewhart control chart is drawn for performing statistical process control.

Details

Package: Copula.Markov
Type: Package
Version: 2.8
Date: 2020-2-25
License: GPL-2

Author(s)

Emura T, Huang XW, Chen WR, Long TH, Sun LH. Maintainer: Takeshi Emura <takeshiemura@gmail.com>

References

Chen W (2018) Copula-based Markov chain model with binomial data, NCU Library

Huang XW, Chen W, Emura T (2019-), Likelihood-based inference for a copula-based Markov chain model with binomial time series, in review

Emura T, Long TH, Sun LH (2017), R routines for performing estimation and statistical process control under copula-based time series models, Communications in Statistics - Simulation and Computation, 46 (4): 3067-87

Long TH and Emura T (2014), A control chart using copula-based Markov chain models, Journal of the Chinese Statistical Association 52 (No.4): 466-96

Lin WC, Emura T, Sun LH (2019), Estimation under copula-based Markov normal mixture models for serially correlated data, Communications in Statistics - Simulation and Computation, doi:10.1080/03610918.2019.1652318

Huang XW, Emura T (2019), Model diagnostic procedures for copula-based Markov chain models for statistical process control, Communications in Statistics - Simulation and Computation, doi:10.1080/03610918.2019.1602647


[Package Copula.Markov version 2.8 Index]