TestIndSerCopula: 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 univariate time series

Description

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

Usage

TestIndSerCopula(
  x,
  p,
  trunc.level = 2,
  B = 1000,
  par = FALSE,
  ncores = 2,
  graph = FALSE
)

Arguments

x	Time series
p	Number of consecutive observations
trunc.level	Only subsets of cardinality <= trunc.level (default=2) are considered for the Moebius statistics.
B	Number of multipliers samples (default = 1000)
par	Set to TRUE if one prefers paraller computing (slower)
ncores	Number of cores for parallel computing (default = 2)
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, Sn, and test combinations Tn and Tn2 (only pairs)
pvalue	Approximated P-values for the tests using Gaussian multipliers
card	Cardinaly of the subsets for the Moebius statistics
subsets	Subsets for the Moebius statistics

References

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

Examples

X <- SimAR1Poisson(c(5,0.2),100)
out <- TestIndSerCopula(X,5,3)

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