logLH_HR

Computes the full (censored) log-likelihood of HR model.

internal

Statistical methodology for sparse multivariate extreme value models. Methods are
provided for exact simulation and statistical inference for multivariate Pareto distributions
on graphical structures as described in the paper 'Graphical Models for Extremes' by
Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.

Sebastian Engelke

graphicalExtremes

Statistical Methodology for Graphical Extreme Value Models

Adrien S. Hitz

Nicola Gnecco

Manuel Hentschel

logLH_HR function

<dl><dt>data</dt>
<dd>Numeric \(n \times d\) matrix, containing
observations following a multivariate HR Pareto distribution.</dd>
<dt>Gamma</dt>
<dd>Numeric \(d \times d\) matrix, representing a variogram matrix \(\Gamma\).</dd>
<dt>cens</dt>
<dd>Boolean. If true, then censored log-likelihood is computed.
By default, <code>cens = FALSE</code>.</dd></dl>

Arguments

Full censored log-likelihood of HR model — logLH_HR

<dl>

<dt>data</dt>
<dd>Numeric \(n \times d\) matrix, containing
observations following a multivariate HR Pareto distribution.</dd>


<dt>Gamma</dt>
<dd>Numeric \(d \times d\) matrix, representing a variogram matrix \(\Gamma\).</dd>


<dt>cens</dt>
<dd>Boolean. If true, then censored log-likelihood is computed.
By default, <code>cens = FALSE</code>.</dd>

</dl>

logLH_HR: Full censored log-likelihood of HR model

Description

Usage

Value

Arguments