kdens_symmat

Given a sample of <code>m</code> points <code>xs</code> from an original sample
and a set of <code>n</code> new sample symmetric positive definite matrices <code>x</code> at which to evaluate the kernel, return the density with bandwidth parameter <code>b</code>.

Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) <doi:10.48550/arXiv.2506.08816>, Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection.

Leo Belzile

Kernel Density Estimation for Random Symmetric Positive Definite
Matrices

Frederic Ouimet

kdens_symmat function

<dl><dt>x</dt>
<dd>cube of size <code>d</code> by <code>d</code> by <code>n</code> of points at which to evaluate the density</dd>
<dt>xs</dt>
<dd>cube of size <code>d</code> by <code>d</code> by <code>m</code> of sample matrices which are used to construct the kernel</dd>
<dt>kernel</dt>
<dd>string, one of <code>Wishart</code>, <code>smnorm</code> or <code>smlnorm</code>.</dd>
<dt>b</dt>
<dd>positive double giving the bandwidth parameter</dd>
<dt>log</dt>
<dd>bool; if <code>TRUE</code>, return the log density</dd></dl>

Arguments

Kernel density estimators for symmetric matrices — kdens_symmat

<dl>

<dt>x</dt>
<dd>cube of size <code>d</code> by <code>d</code> by <code>n</code> of points at which to evaluate the density</dd>


<dt>xs</dt>
<dd>cube of size <code>d</code> by <code>d</code> by <code>m</code> of sample matrices which are used to construct the kernel</dd>


<dt>kernel</dt>
<dd>string, one of <code>Wishart</code>, <code>smnorm</code> or <code>smlnorm</code>.</dd>


<dt>b</dt>
<dd>positive double giving the bandwidth parameter</dd>


<dt>log</dt>
<dd>bool; if <code>TRUE</code>, return the log density</dd>

</dl>

kdens_symmat: Kernel density estimators for symmetric matrices

Description

Usage

Value

Arguments