cpAutocop  R Documentation 
Nonparametric test for changepoint detection particularly sensitive to changes in the autocopula of univariate continuous observations. Approximate pvalues for the test statistic are obtained by means of a multiplier approach. Details can be found in the first reference.
cpAutocop(x, lag = 1, b = NULL, bivariate = FALSE, weights = c("parzen", "bartlett"), m = 5, N = 1000, init.seq = NULL, include.replicates = FALSE)
x 
a onecolumn matrix containing continuous observations. 
lag 
an integer specifying at which lag to consider the
autocopula; the autocopula is a ( 
b 
strictly positive integer specifying the value of the
bandwidth parameter determining the serial dependence when
generating dependent multiplier sequences using the 'moving average
approach'; see Section 5 of the second reference. If set to

bivariate 
a logical specifying whether the test should focus
only on the bivariate margin of the ( 
weights 
a string specifying the kernel for creating the weights used in the generation of dependent multiplier sequences within the 'moving average approach'; see Section 5 of the second reference. 
m 
a strictly positive integer specifying the number of points of the uniform grid on (0,1) involved in the estimation of the bandwidth parameter; see Section 5 of the second reference. 
N 
number of multiplier replications. 
init.seq 
a sequence of independent standard normal variates of
length 
include.replicates 
a logical specifying whether the
object of 
The approximate pvalue is computed as
(0.5 + sum(S[i] >= S, i=1, .., N)) / (N+1),
where S and S[i] denote the test statistic and a multiplier replication, respectively. This ensures that the approximate pvalue is a number strictly between 0 and 1, which is sometimes necessary for further treatments.
An object of class
htest
which is a list,
some of the components of which are
statistic 
value of the test statistic. 
p.value 
corresponding approximate pvalue. 
cvm 
the values of the 
b 
the value of parameter 
This is a tests for a continuous univariate time series.
A. Bücher, J.D. Fermanian and I. Kojadinovic (2019), Combining cumulative sum changepoint detection tests for assessing the stationarity of univariate time series, Journal of Time Series Analysis 40, pages 124150, https://arxiv.org/abs/1709.02673.
A. Bücher and I. Kojadinovic (2016), A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing, Bernoulli 22:2, pages 927968, https://arxiv.org/abs/1306.3930.
cpAutocov()
for a related test based on
the autocovariance.
## AR1 example n < 200 k < n/2 ## the true changepoint x < matrix(c(arima.sim(list(ar = 0.5), n = k), arima.sim(list(ar = 0.5), n = n  k))) cp < cpAutocop(x) cp ## Estimated changepoint which(cp$cvm == max(cp$cvm)) ## AR2 example n < 200 k < n/2 ## the true changepoint x < matrix(c(arima.sim(list(ar = c(0,0.5)), n = k), arima.sim(list(ar = c(0,0.5)), n = n  k))) cpAutocop(x) cpAutocop(x, lag = 2) cpAutocop(x, lag = 2, bivariate = TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.