dmbeta2

Given a random matrix <code>x</code>, compute the density
for arguments <code>shape1</code> and <code>shape2</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

dmbeta2 function

<dl><dt>x</dt>
<dd>cube of dimension <code>d</code> by <code>d</code> by <code>n</code> containing the random matrix samples</dd>
<dt>shape1</dt>
<dd>positive shape parameter, strictly larger than \((d-1)/2\).</dd>
<dt>shape2</dt>
<dd>positive shape parameter, strictly larger than \((d-1)/2\).</dd>
<dt>log</dt>
<dd>[logical] if <code>TRUE</code> (default), returns the log density.</dd></dl>

Arguments

Matrix beta type II density function — dmbeta2

<dl>

<dt>x</dt>
<dd>cube of dimension <code>d</code> by <code>d</code> by <code>n</code> containing the random matrix samples</dd>


<dt>shape1</dt>
<dd>positive shape parameter, strictly larger than \((d-1)/2\).</dd>


<dt>shape2</dt>
<dd>positive shape parameter, strictly larger than \((d-1)/2\).</dd>


<dt>log</dt>
<dd>[logical] if <code>TRUE</code> (default), returns the log density.</dd>

</dl>

dmbeta2: Matrix beta type II density function

Description

Usage

Value

Arguments