lcv_kern_smnorm

Given a cube <code>x</code> and a bandwidth <code>b</code>, compute
the leave-one-out cross validation criterion by taking out a slice
and evaluating the kernel at the holdout value.

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

lcv_kern_smnorm 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></dl>

Arguments

Likelihood cross validation criterion for symmetric matrix normal kernel — lcv_kern_smnorm

<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>

</dl>

lcv_kern_smnorm: Likelihood cross validation criterion for symmetric matrix normal kernel

Description

Usage

Value

Arguments