simu_ise_montecarlo

Given a target density and a kernel estimator, evaluate the
integrated squared error by Monte Carlo integration by simulating
from uniform variates on the hypercube.

internal

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

simu_ise_montecarlo function

<dl><dt>x</dt>
<dd>a cube of dimension <code>d</code> by <code>d</code> by <code>n</code> containing the sample matrices which define the kernel matrix estimator</dd>
<dt>b</dt>
<dd>positive double, bandwidth parameter</dd>
<dt>model</dt>
<dd>integer between 1 and 6 indicating the simulation scenario</dd>
<dt>B</dt>
<dd>number of Monte Carlo replications, default to 10K</dd>
<dt>delta</dt>
<dd>double less than 1; the integrals on \([0, \infty)\) are truncated to \([\delta, 1/\delta]\).</dd></dl>

Arguments

Integrated squared error via Monte Carlo — simu_ise_montecarlo

<dl>

<dt>x</dt>
<dd>a cube of dimension <code>d</code> by <code>d</code> by <code>n</code> containing the sample matrices which define the kernel matrix estimator</dd>


<dt>b</dt>
<dd>positive double, bandwidth parameter</dd>


<dt>model</dt>
<dd>integer between 1 and 6 indicating the simulation scenario</dd>


<dt>B</dt>
<dd>number of Monte Carlo replications, default to 10K</dd>


<dt>delta</dt>
<dd>double less than 1; the integrals on \([0, \infty)\) are truncated to \([\delta, 1/\delta]\).</dd>

</dl>

simu_ise_montecarlo: Integrated squared error via Monte Carlo

Description

Usage

Value

Arguments