test.change.point {changepointTests}

Function to perform changepoint tests with multiplier bootstrap using the usual sequential process

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.

Usage

test.change.point(
  x,
  N = 1000,
  n_cores = 2,
  boot.method = "multipliers",
  est = FALSE
)

Arguments

x

(n x d) matrix of data (observations or pseudo-observations, including residuals), d>=1

N

number of multipliers samples to compute the P-value

n_cores

number of cores for parallel computing (default = 2)

boot.method

bootstrapping method: 'multipliers' (default, fastest) or 'bootstrap'

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.

Examples

x=matrix(rnorm(600),ncol=3)
out = test.change.point(x)