lscv_kern_smlnorm

Finite sample h-block leave-one-out approximation to the least
square criterion, omitting constant term. Only pairs that are \(|i-j| \leq h\) apart are considered.

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

lscv_kern_smlnorm 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>h</dt>
<dd>[int] integer indicating the separation lag</dd></dl>

Arguments

Least square cross validation criterion for log symmetric matrix normal kernel — lscv_kern_smlnorm

<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>h</dt>
<dd>[int] integer indicating the separation lag</dd>

</dl>

lscv_kern_smlnorm: Least square cross validation criterion for log symmetric matrix normal kernel

Description

Usage

Value

Arguments