test_op_bootstrap

docblock

test_op_bootstrap(data, n_boot; chart_choice, m=3, d=1, alpha=0.05, block_size=1)

Compute a bootstrap hypothesis test for ordinal patterns and return an OPTestResultBoot with the bootstrap critical value, p-value, and reject decision.

Unlike test_op(), this function does not rely on asymptotic distributions and therefore works for any pattern length m, including m > 3 where no asymptotic theory is available.

data: the time series.
n_boot: number of bootstrap replications.
chart_choice: one of Persistence(), UpDownBalance(), RotationalAsymmetry(), UpDownScaling(), DistanceToWhiteNoise(), Shannon(), ShannonExtropy(). For Shannon and ShannonExtropy, the statistic is in the logarithm base of the chart, which must be larger than 1. Both default to base 2 in ComplexityMeasures.jl; use Shannon(base=exp(1)) for the natural logarithm used in the papers. Statistic and critical value are both in that base, so the test decision and the p-value do not depend on it.
alpha: significance level (default 0.05).
block_size: set > 1 for a block bootstrap that preserves serial dependencies.