Clayton.Markov.GOF.binom: A goodness-of-fit test for the marginal binomial...

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

Description

Perform a parametric bootstrap test based on the Cramer-von Mises and Kolmogorov-Smirnov statistics as proposed by Huang and Emura (2019) and Huang et al. (2019-).

Usage

Clayton.Markov.GOF.binom(Y, k = 3, size, B = 200,GOF.plot=FALSE, method = "Newton")

Arguments

Y	vector of datasets
k	constant determining the length between LCL and UCL (k=3 corresponds to 3-sigma limit)
size	number of binomial trials
B	the number of Bootstrap replications
GOF.plot	if TRUE, show the model diagnostic plots for B bootstrap replications
method	Newton-Raphson method or nlm can be chosen

Value

CM	The Cramer-von Mises statistic and its P-value
KS	The Kolmogorov-Smirnov statistic and its P-value
CM.boot	Bootstrap values of the Cramer-von Mises statistics
KS.boot	Bootstrap values of the Kolmogorov-Smirnov statistics

Author(s)

Huang XW, Emura T

References

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

Huang XW, Emura T (2021-), Computational methods for a copula-based Markov chain model with a binomial time series, in review

Examples

size=50
prob=0.5
alpha=2
set.seed(1)
Y=Clayton.Markov.DATA.binom(n=500,size,prob,alpha)
Clayton.Markov.GOF.binom(Y,size=size,B=5,k=3,GOF.plot=TRUE) ## B=5 to save time

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