tnorm_loo

Given a data matrix, 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.

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_loo function

<dl><dt>x</dt>
<dd><code>n</code> by <code>d</code> matrix of observations</dd>
<dt>Omega</dt>
<dd>smoothing positive-definite matrix</dd>
<dt>beta</dt>
<dd>vector of constraints for the half-space</dd></dl>

Arguments

Leave-one-out cross-validation for truncated Gaussian kernel density estimation — tnorm_loo

<dl>

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


<dt>Omega</dt>
<dd>smoothing positive-definite matrix</dd>


<dt>beta</dt>
<dd>vector of constraints for the half-space</dd>

</dl>

tnorm_loo: Leave-one-out cross-validation for truncated Gaussian kernel density estimation

Description

Usage

Value

Arguments