logLH_HR

Numeric matrix \(n\times d\). It contains
observations following a multivariate HR Pareto distribution.

data

Numeric matrix \(n\times d\).
It represents a variogram matrix \(\Gamma\).

Gamma

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

cens

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

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 (2018) <arXiv:1812.01734>.

Sebastian Engelke

graphicalExtremes

Statistical Methodology for Graphical Extreme Value Models

Adrien S. Hitz

Nicola Gnecco

logLH_HR function

<dl><dt>data</dt>
<dd>Numeric matrix \(n\times d\). It contains
observations following a multivariate HR Pareto distribution.</dd>
<dt>Gamma</dt>
<dd>Numeric matrix \(n\times d\).
It represents 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 matrix \(n\times d\). It contains
observations following a multivariate HR Pareto distribution.</dd>


<dt>Gamma</dt>
<dd>Numeric matrix \(n\times d\).
It represents 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