mig_kdens

Given a matrix of new observations, compute the density of the multivariate
inverse Gaussian mixture defined by assigning equal weight to each component where
\(\boldsymbol{\xi}\) is the location parameter.

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_kdens function

<dl><dt>x</dt>
<dd><code>n</code> by <code>d</code> matrix of quantiles</dd>
<dt>newdata</dt>
<dd>matrix of new observations at which to evaluated the kernel density</dd>
<dt>Omega</dt>
<dd><code>d</code> by <code>d</code> positive definite scale matrix \(\boldsymbol{\Omega}\)</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>log</dt>
<dd>logical; if <code>TRUE</code>, returns log probabilities</dd></dl>

Arguments

Multivariate inverse Gaussian kernel density estimator — mig_kdens

<dl>

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


<dt>newdata</dt>
<dd>matrix of new observations at which to evaluated the kernel density</dd>


<dt>Omega</dt>
<dd><code>d</code> by <code>d</code> positive definite scale matrix \(\boldsymbol{\Omega}\)</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>log</dt>
<dd>logical; if <code>TRUE</code>, returns log probabilities</dd>

</dl>

mig_kdens: Multivariate inverse Gaussian kernel density estimator

Description

Usage

Value

Arguments