Clayton.Markov.DATA: Generating Time Series Data Under a Copula-Based Markov Chain...

In Copula.Markov: Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series

Description

Time-series data are generated under a copula-based Markov chain model with the Clayton copula. See Long et al. (2014) and Emura et al. (2017) for the details.

Usage

Clayton.Markov.DATA(n, mu, sigma, alpha)

Arguments

n	sample size
mu	mean
sigma	standard deviation
alpha	association parameter

Details

-1<alpha<0 for negative association; alpha>0 for positive association

Value

Time series data of size n.

Author(s)

Takeshi Emura

References

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

Examples

set.seed(1)
Y=Clayton.Markov.DATA(n=1000,mu=0,sigma=1,alpha=8)
Clayton.Markov.MLE(Y,plot=TRUE)

Copula.Markov documentation built on Nov. 29, 2021, 9:07 a.m.