dsmlnorm

Density of the lognormal matrix-variate density, defined through the matrix logarithm, with the Jacobian resulting from the transformation

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

dsmlnorm function

<dl><dt>x</dt>
<dd>[cube] array of dimension <code>d</code> by <code>d</code> by <code>n</code></dd>
<dt>b</dt>
<dd>[numeric] scale parameter, strictly positive</dd>
<dt>M</dt>
<dd>[matrix] location matrix, positive definite</dd>
<dt>log</dt>
<dd>[logical] if <code>TRUE</code> (default), returns the log density</dd></dl>

Arguments

Symmetric matrix-variate lognormal density — dsmlnorm

<dl>

<dt>x</dt>
<dd>[cube] array of dimension <code>d</code> by <code>d</code> by <code>n</code></dd>


<dt>b</dt>
<dd>[numeric] scale parameter, strictly positive</dd>


<dt>M</dt>
<dd>[matrix] location matrix, positive definite</dd>


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

</dl>

dsmlnorm: Symmetric matrix-variate lognormal density

Description

Usage

Value

Arguments