logdV_HR

Computes the exponent measure density of HR distribution.

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

logdV_HR function

<dl><dt>x</dt>
<dd>Numeric \(n \times d\) matrix or vector with <code>d</code> elements.</dd>
<dt>Gamma</dt>
<dd>d x d variogram matrix or numeric vector with d(d-1)/2 elements,
containing the upper triangular part of a variogram matrix.</dd>
<dt>Theta</dt>
<dd>d x d precision matrix or numeric vector with d(d-1)/2 elements,
containing the upper triangular part of a precision matrix.</dd></dl>

Arguments

Compute the exponent measure density of HR distribution — logdV_HR

<dl>

<dt>x</dt>
<dd>Numeric \(n \times d\) matrix or vector with <code>d</code> elements.</dd>


<dt>Gamma</dt>
<dd>d x d variogram matrix or numeric vector with d(d-1)/2 elements,
containing the upper triangular part of a variogram matrix.</dd>


<dt>Theta</dt>
<dd>d x d precision matrix or numeric vector with d(d-1)/2 elements,
containing the upper triangular part of a precision matrix.</dd>

</dl>

logdV_HR: Compute the exponent measure density of HR distribution

Description

Usage

Value

Arguments

Details