Q_WS_hyp_test: Compute size alpha single-lag hypothesis test under weak or... In wwntests: Hypothesis Tests for Functional Time Series

Description

`Q_WS_hyp_test` Computes the size alpha test of a single lag hypothesis under a weak white noise or strong white noise assumption using a Welch-Satterthwaite Approximation.

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```Q_WS_hyp_test( f_data, lag, alpha = 0.05, iid = FALSE, M = NULL, low_disc = FALSE, bootstrap = FALSE, block_size = "adaptive", straps = 300, moving = 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 `iid` boolean value, if given TRUE, the hypothesis test will use a strong-white noise assumption. By default is FALSE, in which the hypothesis test will use a weak-white noise assumption. `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. `bootstrap` boolean value, if given TRUE, the hypothesis test is done by approximating the limiting distribution of the test statistic via a block bootstrap algorithm. FALSE by default `block_size` the block size to be used in the block bootstrap method (in each bootstrap sample). 10 by default. `straps` the number of bootstrap samples to take; 300 by default `moving` boolean value; determines whether or not the block bootstrap should be moving

Value

A list containing the p-value, the quantile, and a boolean value indicating whether or not the hypothesis is rejected.

wwntests documentation built on July 2, 2020, 2:57 a.m.