Clayton.Markov.GOF.binom {Copula.Markov} R Documentation

## A goodness-of-fit test for the marginal binomial distribution.

### 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 (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

Huang XW, Chen W, Emura T (2019-), Likelihood-based inference for a copula-based Markov chain model with 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
```

[Package Copula.Markov version 2.8 Index]