Joe.Markov.MLE {Copula.Markov} | R Documentation |
Maximum Likelihood Estimation and Statistical Process Control Under the Joe Copula
Description
The maximum likelihood estimates are produced and the Shewhart control chart is drawn with k-sigma control limits (e.g., 3-sigma). The dependence model follows the Joe copula and the marginal (stationary) distribution follows the normal distribution.
Usage
Joe.Markov.MLE(Y, k = 3, D = 1, plot = TRUE,GOF=FALSE,method = "nlm")
Arguments
Y |
vector of datasets |
k |
constant determining the length between LCL and UCL (k=3 corresponds to 3-sigma limit) |
D |
diameter for U(-D, D) used in randomized Newton-Raphson |
plot |
show the control chart if TRUE |
GOF |
show the model diagnostic plot if TRUE |
method |
apply "nlm" or "Newton" method |
Value
mu |
estimate, SE, and 95 percent CI |
sigma |
estimate, SE, and 95 percent CI |
alpha |
estimate, SE, and 95 percent CI |
Control_Limit |
Center = mu, LCL = mu - k*sigma, UCL = mu + k*sigma |
out_of_control |
IDs for out-of-control points |
Gradient |
gradients (must be zero) |
Hessian |
Hessian matrix |
Eigenvalue_Hessian |
Eigenvalues for the Hessian matrix |
KS.test |
KS statistics |
CM.test |
CM statistics |
log.likelihood |
Log-likelihood value for the estimation |
Author(s)
Long TH, Huang XW and 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
n=1000
alpha=2.856 ### Kendall's tau =0.5 ###
mu=2
sigma=1
Y=Joe.Markov.DATA(n,mu,sigma,alpha)
mean(Y)
sd(Y)
cor(Y[-1],Y[-n],method="kendall")
Joe.Markov.MLE(Y,k=2)