stDistAutocop  R Documentation 
A nonparametric test of stationarity for univariate continuous time
series resulting from a combination à la Fisher of the
changepoint test sensitive to changes in the distribution function
implemented in cpDist()
and the changepoint test
sensitive to changes in the autcopula implemented in
cpAutocop()
. Approximate pvalues are obtained by
combining two multiplier resampling schemes. Details can be
found in the first reference.
stDistAutocop(x, lag = 1, b = NULL, pairwise = FALSE, weights = c("parzen", "bartlett"), m = 5, N = 1000)
x 
a onecolumn matrix containing continuous observations. 
lag 
an integer specifying at which lag to consider the
autocopula; the autcopula 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

pairwise 
a logical specifying whether the test should focus
only on the bivariate margins 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. 
The testing procedure is described in detail in the second section of the first reference.
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 à Fisher. 
component.p.values 
pvalues of the component tests arising in the combination. 
b 
the value of parameter 
This is a test for 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.
see cpDist()
and cpAutocop()
for the
component tests.
## AR1 example n < 200 k < n/2 ## the true changepoint x < matrix(c(arima.sim(list(ar = 0.1), n = k), arima.sim(list(ar = 0.5), n = n  k))) stDistAutocop(x) ## AR2 example n < 200 k < n/2 ## the true changepoint x < matrix(c(arima.sim(list(ar = c(0,0.1)), n = k), arima.sim(list(ar = c(0,0.5)), n = n  k))) ## Not run: stDistAutocop(x) stDistAutocop(x, lag = 2) ## End(Not run) stDistAutocop(x, lag = 2, pairwise = TRUE)
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