| 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: | 2.9 |
| Date: | 2021-11-29 |
| 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, Emura T (2021-), Computational methods for a copula-based Markov chain model with a 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(4):466-96
Lin WC, Emura T, Sun LH (2021), Estimation under copula-based Markov normal mixture models for serially correlated data, Communications in Statistics - Simulation and Computation, 50(12):4483-515
Huang XW, Emura T (2021), Model diagnostic procedures for copula-based Markov chain models for statistical process control, Communications in Statistics - Simulation and Computation, doi: 50(8):2345-67