gauss_rlcv

Robust likelihood cross-validation criterion function of Wu.

internal

Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.

Leo Belzile

Multivariate Inverse Gaussian Distribution

Frederic Ouimet

gauss_rlcv function

<dl><dt>x</dt>
<dd><code>n</code> by <code>d</code> matrix of observations</dd>
<dt>Sigma</dt>
<dd>smoothing positive-definite matrix</dd>
<dt>logweights</dt>
<dd>log vector of weights</dd>
<dt>an</dt>
<dd>threshold for linear approximation</dd>
<dt>xsamp</dt>
<dd><code>n</code> by <code>d</code> random sample for Monte Carlo estimation of bias</dd>
<dt>dxsamp</dt>
<dd><code>n</code> vector of density for the points from <code>xsamp</code></dd>
<dt>mckern</dt>
<dd>logical; if <code>TRUE</code>, uses the kernel as sampler for Monte Carlo estimation</dd></dl>

Arguments

Robust likelihood cross-validation for Gaussian kernel density estimation — gauss_rlcv

<dl>

<dt>x</dt>
<dd><code>n</code> by <code>d</code> matrix of observations</dd>


<dt>Sigma</dt>
<dd>smoothing positive-definite matrix</dd>


<dt>logweights</dt>
<dd>log vector of weights</dd>


<dt>an</dt>
<dd>threshold for linear approximation</dd>


<dt>xsamp</dt>
<dd><code>n</code> by <code>d</code> random sample for Monte Carlo estimation of bias</dd>


<dt>dxsamp</dt>
<dd><code>n</code> vector of density for the points from <code>xsamp</code></dd>


<dt>mckern</dt>
<dd>logical; if <code>TRUE</code>, uses the kernel as sampler for Monte Carlo estimation</dd>

</dl>

gauss_rlcv: Robust likelihood cross-validation for Gaussian kernel density estimation

Description

Usage

Value

Arguments