tnorm_kdens_arma

Given a data matrix over a half-space defined by <code>beta</code>,
compute the log density of the asymmetric truncated Gaussian kernel density estimator,
taking in turn an observation as location vector.

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

tnorm_kdens_arma 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></dl>

Arguments

Truncated Gaussian kernel density estimator — tnorm_kdens_arma

<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>

</dl>

tnorm_kdens_arma: Truncated Gaussian kernel density estimator

Description

Usage

Value

Arguments