TestIndSerCopulaMulti: Statistics and P-values for a test of randomness for a...

In MixedIndTests: Tests of Randomness and Tests of Independence

Statistics and P-values for a test of randomness for a multivariate time series

Description

This function computes Cramer-von Mises statistics from the multilinear copula and their combination for a tests of randomness for p consecutives values of random vectors X(1), ..., X(p). The p-values are computed using Gaussian multipliers.

Usage

TestIndSerCopulaMulti(x, p, trunc.level = 2, B = 1000, graph = FALSE)

Arguments

x	Time series matrix
p	Number of consecutive vectors
trunc.level	Only subsets of cardinality <= trunc.level (default=2) are considered for the Moebius statistics.
B	Number of multipliers samples (default = 1000)
graph	Set to TRUE if one wants the dependogram of P-values for the Moebius statistics

Value

stat	List of Cramer-von Mises statistics cvm, tilde Sn, and test combinations tilde Tn and tilde Tn2 (only pairs), as defined in Nasri(2022).
pvalue	Approximated P-values for the tests using Gaussian multipliers

References

B.R Nasri (2022). Tests of serial dependence for arbitrary distributions

Examples

data(Y)
out <- TestIndSerCopulaMulti(Y,5,5)

MixedIndTests documentation built on May 29, 2024, 12:19 p.m.