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

Description Usage Arguments Value

View source: R/hypothesis_quantiles.R

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

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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.