rtellipt

Simulate multivariate Student-t \(\boldsymbol{x}\)
with location vector <code>mu</code>, scale matrix <code>sigma</code> and <code>df</code> (integer) degrees of freedom
subject to the linear constraint \(\boldsymbol{\beta}^\top\boldsymbol{x} &gt; 0\).
Negative degrees of freedom or values larger than 1000 imply Gaussian vectors are generated instead.

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

rtellipt function

<dl><dt>n</dt>
<dd>number of simulations</dd>
<dt>beta</dt>
<dd><code>d</code> vector of linear constraints</dd>
<dt>mu</dt>
<dd>location vector</dd>
<dt>sigma</dt>
<dd>scale matrix</dd>
<dt>df</dt>
<dd>degrees of freedom argument</dd>
<dt>delta</dt>
<dd>buffer; default to zero</dd></dl>

Arguments

Simulate elliptical vector subject to a linear constraint — rtellipt

<dl>

<dt>n</dt>
<dd>number of simulations</dd>


<dt>beta</dt>
<dd><code>d</code> vector of linear constraints</dd>


<dt>mu</dt>
<dd>location vector</dd>


<dt>sigma</dt>
<dd>scale matrix</dd>


<dt>df</dt>
<dd>degrees of freedom argument</dd>


<dt>delta</dt>
<dd>buffer; default to zero</dd>

</dl>

rtellipt: Simulate elliptical vector subject to a linear constraint

Description

Usage

Value

Arguments