mig_rlcv

Given a data matrix over a half-space defined by <code>beta</code>,
compute the log density using leave-one-out cross validation,
taking in turn an observation as location vector and computing the
density of the resulting mixture.

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

mig_rlcv function

<dl><dt>x</dt>
<dd><code>n</code> by <code>d</code> matrix of quantiles</dd>
<dt>beta</dt>
<dd><code>d</code> vector \(\boldsymbol{\beta}\) defining the half-space through \(\boldsymbol{\beta}^{\top}\boldsymbol{\xi}&gt;0\)</dd>
<dt>Omega</dt>
<dd><code>d</code> by <code>d</code> positive definite scale matrix \(\boldsymbol{\Omega}\)</dd>
<dt>xsamp</dt>
<dd>matrix of points at which to evaluate the integral</dd>
<dt>dxsamp</dt>
<dd>density of points</dd></dl>

Arguments

Robust likelihood cross-validation for kernel density estimation — mig_rlcv

<dl>

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


<dt>beta</dt>
<dd><code>d</code> vector \(\boldsymbol{\beta}\) defining the half-space through \(\boldsymbol{\beta}^{\top}\boldsymbol{\xi}&gt;0\)</dd>


<dt>Omega</dt>
<dd><code>d</code> by <code>d</code> positive definite scale matrix \(\boldsymbol{\Omega}\)</dd>


<dt>xsamp</dt>
<dd>matrix of points at which to evaluate the integral</dd>


<dt>dxsamp</dt>
<dd>density of points</dd>

</dl>

mig_rlcv: Robust likelihood cross-validation for kernel density estimation

Description

Usage

Value

Arguments