B_h_bound: Compute weak white noise confidence bound for autocorrelation...

In dpetoukhov/wwntests: Hypothesis Tests for Functional Time Series

Description

B_h_bound Computes an approximate asymptotic upper 1-alpha confidence bound for the functional autocorrelation coefficient at lag h under a weak white noise assumption.

Usage

B_h_bound(f_data, lag, alpha = 0.05, M = NULL, low_disc = FALSE)

Arguments

f_data	the functional data matrix with observed functions in the columns
lag	the lag to use to compute the single lag test statistic
alpha	the significance level to be used in the hypothesis test
M	Number of samples to take when applying a Monte-Carlo approximation
low_disc	Boolean value indicating whether or not to use low-discrepancy sampling in the Monte Carlo method. Note, low-discrepancy sampling will yield deterministic results.

Value

numeric value; the 1-alpha confidence bound for the functional autocorrelation coefficient at lag h under a weak white noise assumption.

dpetoukhov/wwntests documentation built on Jan. 3, 2020, 12:14 a.m.