Description Usage Arguments Value

View source: R/hypothesis_quantiles.R

`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.

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`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 |

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

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