stDistAutocop | R Documentation |
A nonparametric test of stationarity for univariate continuous time
series resulting from a combination à la Fisher of the
change-point test sensitive to changes in the distribution function
implemented in cpDist()
and the change-point test
sensitive to changes in the autcopula implemented in
cpAutocop()
. Approximate p-values 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 one-column 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 p-value à Fisher. |
component.p.values |
p-values 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 change-point detection tests for assessing the stationarity of univariate time series, Journal of Time Series Analysis 40, pages 124-150, 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 927-968, https://arxiv.org/abs/1306.3930.
see cpDist()
and cpAutocop()
for the
component tests.
## AR1 example n <- 200 k <- n/2 ## the true change-point 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 change-point 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.