scalar_covariance_i_j

`scalar_covariance_i_j` computes the approximate covariance at a point of the functional data
for lag windows defined by i,j; a scalarized version of covariance_i_j that takes point estimates.

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

scalar_covariance_i_j function

<dl><dt>f_data</dt>
<dd>the functional data matrix with observed functions in the columns</dd>
<dt>i, j</dt>
<dd>the indices i,j in 1:T that we are computing the covariance for</dd>
<dt>times</dt>
<dd>A vector with 4 columns containing indices specifying which subset of f_data to consider</dd></dl>

Arguments

Compute the approximate covariance at a point for lag windows defined by i,j — scalar_covariance_i_j

<dl>

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


<dt>i, j</dt>
<dd>the indices i,j in 1:T that we are computing the covariance for</dd>


<dt>times</dt>
<dd>A vector with 4 columns containing indices specifying which subset of f_data to consider</dd>

</dl>

scalar_covariance_i_j: Compute the approximate covariance at a point for lag windows defined by i,j

Description

Usage

Value

Arguments