B_h_bound

`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.

Provides a collection of white noise hypothesis tests for functional time series and related visualizations.
These include tests based on the norms of autocovariance operators that are built under both strong and weak
white noise assumptions. Additionally, tests based on the spectral density operator and on principal component
dimensional reduction are included, which are built under strong white noise assumptions.
Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models.
These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019)
<doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>,
and Kim et al. (2023) <doi: 10.1214/23-SS143>
respectively.

Mihyun Kim

wwntests

Hypothesis Tests for Functional Time Series

Daniel Petoukhov

B_h_bound function

<dl><dt>f_data</dt>
<dd>the functional data matrix with observed functions in the columns</dd>
<dt>lag</dt>
<dd>the lag to use to compute the single lag test statistic</dd>
<dt>alpha</dt>
<dd>the significance level to be used in the hypothesis test</dd>
<dt>M</dt>
<dd>Number of samples to take when applying a Monte-Carlo approximation</dd></dl>

Arguments

Compute weak white noise confidence bound for autocorrelation coefficient. — B_h_bound

<dl>

<dt>f_data</dt>
<dd>the functional data matrix with observed functions in the columns</dd>


<dt>lag</dt>
<dd>the lag to use to compute the single lag test statistic</dd>


<dt>alpha</dt>
<dd>the significance level to be used in the hypothesis test</dd>


<dt>M</dt>
<dd>Number of samples to take when applying a Monte-Carlo approximation</dd>

</dl>

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

Description

Usage

Value

Arguments