hsgauss_kdens

Given a data matrix over a half-space defined by <code>beta</code>, compute an homeomorphism to
\(\mathbb{R}^d\) and perform kernel smoothing based on a Gaussian kernel density estimator,
taking each turn an observation as location vector.

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

hsgauss_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>Sigma</dt>
<dd>scale matrix</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>
<dt>...</dt>
<dd>additional arguments, currently ignored</dd></dl>

Arguments

Gaussian kernel density estimator on half-space — hsgauss_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>Sigma</dt>
<dd>scale matrix</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>


<dt>...</dt>
<dd>additional arguments, currently ignored</dd>

</dl>

hsgauss_kdens: Gaussian kernel density estimator on half-space

Description

Usage

Value

Arguments