logdVK_HR

Numeric vector with \(d\) positive elements
where the censored exponent measure is to be evaluated.

Integer vector, subset of \(\{1, \dots, d\}\).
The index set that is not censored.

Numeric vector with
\(\frac{d(d - 1)}{2}\) elements.
It represents the upper triangular portion of a
variogram matrix \(\Gamma\).

Computes the censored exponent measure density of HR distribution.

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

logdVK_HR function

<dl><dt>x</dt>
<dd>Numeric vector with \(d\) positive elements
where the censored exponent measure is to be evaluated.</dd>
<dt>K</dt>
<dd>Integer vector, subset of \(\{1, \dots, d\}\).
The index set that is not censored.</dd>
<dt>par</dt>
<dd>Numeric vector with
\(\frac{d(d - 1)}{2}\) elements.
It represents the upper triangular portion of a
variogram matrix \(\Gamma\).</dd></dl>

Arguments

Compute censored exponent measure — logdVK_HR

<dl>

<dt>x</dt>
<dd>Numeric vector with \(d\) positive elements
where the censored exponent measure is to be evaluated.</dd>


<dt>K</dt>
<dd>Integer vector, subset of \(\{1, \dots, d\}\).
The index set that is not censored.</dd>


<dt>par</dt>
<dd>Numeric vector with
\(\frac{d(d - 1)}{2}\) elements.
It represents the upper triangular portion of a
variogram matrix \(\Gamma\).</dd>

</dl>

logdVK_HR: Compute censored exponent measure

Description

Usage

Value

Arguments