brown_motion

`brown_motion` Creates at J x N matrix, containing N independent Brownian motion sample paths in
each of the columns.

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

brown_motion function

<dl><dt>N</dt>
<dd>the number of independent Brownian motion sample paths to compute.</dd>
<dt>J</dt>
<dd>the number of steps observed for each sample path (the resolution of the data).</dd></dl>

Arguments

`brown_motion` Creates at J x N matrix, containing N independent Brownian motion sample paths in
each of the columns. — brown_motion

<dl>

<dt>N</dt>
<dd>the number of independent Brownian motion sample paths to compute.</dd>


<dt>J</dt>
<dd>the number of steps observed for each sample path (the resolution of the data).</dd>

</dl>

brown_motion: `brown_motion` Creates at J x N matrix, containing N independent Brownian motion sample paths in each of the columns.

Description

Usage

Value

Arguments

Examples