LongRunCovMatrix

This function estimates the long run covariance matrix of a given multivariate data sample.

Performs alignment, PCA, and modeling of multidimensional and
unidimensional functions using the square-root velocity framework
(Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014
<DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic
analysis of functional data through phase and amplitude separation.

J Derek Tucker

fdasrvf

Elastic Functional Data Analysis

J. Derek Tucker

Aymeric Stamm

LongRunCovMatrix function

<dl><dt>mdobj</dt>
<dd>A multivariate data object</dd>
<dt>h</dt>
<dd>The bandwidth parameter. It is strictly non-zero. Choosing the bandwidth parameter to be zero is identical
to estimating covariance matrix assuming iid data.</dd>
<dt>kern_type</dt>
<dd>Kernel function to be used for the estimation of the long run covariance
matrix. The choices are <code>c("BT", "PR", "SP", "FT")</code> which are respectively, bartlett, parzen, simple and flat-top kernels.
By default the function uses a <code>"barlett"</code> kernel.</dd></dl>

Arguments

Long Run Covariance Matrix Estimation for Multivariate Time Series — LongRunCovMatrix

<dl>

<dt>mdobj</dt>
<dd>A multivariate data object</dd>


<dt>h</dt>
<dd>The bandwidth parameter. It is strictly non-zero. Choosing the bandwidth parameter to be zero is identical
to estimating covariance matrix assuming iid data.</dd>


<dt>kern_type</dt>
<dd>Kernel function to be used for the estimation of the long run covariance
matrix. The choices are <code>c("BT", "PR", "SP", "FT")</code> which are respectively, bartlett, parzen, simple and flat-top kernels.
By default the function uses a <code>"barlett"</code> kernel.</dd>

</dl>

LongRunCovMatrix: Long Run Covariance Matrix Estimation for Multivariate Time Series

Description

Usage

Value

Arguments