test.change.point.copula.BKRS: Function toperform changepoint test for the copula with...

In changepointTests: Change Point Tests for Joint Distributions and Copulas

Function toperform changepoint test for the copula with multiplier bootstrap using for changepoint the new sequential process of Bucher et al (2014)

Description

This function compute the Cramer-von Mises and Kolmogorov-Smirnov test statistics based on the new sequential process of Bucher et al (2014), using multipliers and parallel computing. Two methods of bootstrapping are used: non-sequential (fastest) and sequential. Both methods yields basically the same P-valueas.

Usage

test.change.point.copula.BKRS(
  x,
  N = 1000,
  n_cores = 2,
  method = "nonseq",
  est = FALSE
)

Arguments

x	(n x d) matrix of data (observations or pseudo-observations, including residuals), d >=2
N	number of multipliers samples to compute the P-value
n_cores	number of cores for parallel computing (default = 2)
method	'nonseq' (default) or 'seq'
est	if TRUE, tau is estimated (default = FALSE)

Value

CVM	Cramer-von Mises statistic
KS	Kolmogorov-Smirnov statistic
pvalueCVM	Pvalue for the Cramer-von Mises statistic
pvalueKS	Pvalue for theKolmogorov-Smirnov statistic
tauCVM	Estimated changepoint using the Cramer-von Mises statistic
tauKS	Estimated changepoint using the Kolmogorov-Smirnov statistic

Author(s)

Bouchra R Nasri and Bruno N Remillard, August 6, 2020

References

Nasri, B. R. Remillard, B., & Bahraoui, T. (2022). Change-point problems for multivariate time series using pseudo-observations, J. Multivariate Anal., 187, 104857.

Bucher, A., Kojadinovic, I., Rohmer, T., & Segers, J. (2014). Detecting changes in cross-sectional dependence in multivariate time series, J. Multiv. Anal., 132, 111–128.

Examples

x<-matrix(rnorm(100),ncol=2)
out = test.change.point.copula.BKRS(x)

changepointTests documentation built on Sept. 30, 2024, 9:24 a.m.