block_bootsrap

`block_bootstrap` Performs a block bootstrap on the functional data f_data with block size b.

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

block_bootsrap function

<dl><dt>f_data</dt>
<dd>the functional data matrix with observed functions in the columns</dd>
<dt>b</dt>
<dd>the block size (of each block in each bootstrap sample)</dd>
<dt>B</dt>
<dd>the number of bootstraps samples</dd>
<dt>moving</dt>
<dd>boolean value specifying whether the block bootstrap should be moving or not. A moving black
bootstrap samples individual functional observations and adds on the consequent block, rather than sampling
blocks of the data.</dd></dl>

Arguments

`block_bootstrap` Performs a block bootstrap on the functional data f_data with block size b. — block_bootsrap

<dl>

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


<dt>b</dt>
<dd>the block size (of each block in each bootstrap sample)</dd>


<dt>B</dt>
<dd>the number of bootstraps samples</dd>


<dt>moving</dt>
<dd>boolean value specifying whether the block bootstrap should be moving or not. A moving black
bootstrap samples individual functional observations and adds on the consequent block, rather than sampling
blocks of the data.</dd>

</dl>

block_bootsrap: `block_bootstrap` Performs a block bootstrap on the functional data f_data with block size b.

Description

Usage

Value

Arguments