autocov_approx_h

`autocov_approx_h` computes the approximate autocovariance for a given lag h of the functional
data

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

autocov_approx_h 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></dl>

Arguments

Compute the approximate autocovariance at specified lag — autocov_approx_h

<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>

</dl>

autocov_approx_h: Compute the approximate autocovariance at specified lag

Description

Usage

Value

Arguments