TestIndSerCopula {MixedIndTests}

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)