Q_WS_hyp_test: Compute size alpha single-lag hypothesis test under weak or...

In wwntests: Hypothesis Tests for Functional Time Series

Compute size alpha single-lag hypothesis test under weak or strong white noise assumption

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

Q_WS_hyp_test(
  f_data,
  lag,
  alpha = 0.05,
  iid = FALSE,
  M = NULL,
  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
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 Nov. 1, 2022, 5:05 p.m.