simu_px_HR

Simulates the Huesler-Reiss extremal functions

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

simu_px_HR function

<dl><dt>n</dt>
<dd>Number of simulations.</dd>
<dt>d</dt>
<dd>Dimension of the multivariate Pareto distribution.
In some cases this can be <code>NULL</code> and will be inferred from <code>par</code>.</dd>
<dt>idx</dt>
<dd>Integer. Index corresponding to the variable over which
the extremal function is simulated.</dd>
<dt>trend</dt>
<dd>Numeric. Trend corresponding to the variable <code>idx</code>.</dd>
<dt>chol_mat</dt>
<dd>Numeric matrix \(d \times d\).
Cholesky decomposition of the desired covariance matrix.</dd></dl>

Arguments

Simulate HR extremal functions — simu_px_HR

<dl>

<dt>n</dt>
<dd>Number of simulations.</dd>


<dt>d</dt>
<dd>Dimension of the multivariate Pareto distribution.
In some cases this can be <code>NULL</code> and will be inferred from <code>par</code>.</dd>


<dt>idx</dt>
<dd>Integer. Index corresponding to the variable over which
the extremal function is simulated.</dd>


<dt>trend</dt>
<dd>Numeric. Trend corresponding to the variable <code>idx</code>.</dd>


<dt>chol_mat</dt>
<dd>Numeric matrix \(d \times d\).
Cholesky decomposition of the desired covariance matrix.</dd>

</dl>

simu_px_HR: Simulate HR extremal functions

Description

Usage

Value

Arguments