emtp2

This function implements a block descent algorithm to find the maximum of the
Gaussian log-likelihood under the constraint that the concentration matrix is a Laplacian matrix.
See roe2021;textualgraphicalExtremes for details.

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

emtp2 function

<dl><dt>Gamma</dt>
<dd>conditionally negative semidefinite matrix. This will be typically the empirical variogram matrix.</dd>
<dt>tol</dt>
<dd>The convergence tolerance. The algorithm terminates when the sum of absolute differences between two iterations is below <code>tol</code>.</dd>
<dt>verbose</dt>
<dd>if TRUE (default) the output will be printed.</dd>
<dt>initial_point</dt>
<dd>if TRUE (default), the algorithm will construct an initial point before the iteration steps.</dd></dl>

Arguments

Performs Gaussian likelihood optimization under Laplacian matrix constraints. — emtp2

<dl>

<dt>Gamma</dt>
<dd>conditionally negative semidefinite matrix. This will be typically the empirical variogram matrix.</dd>


<dt>tol</dt>
<dd>The convergence tolerance. The algorithm terminates when the sum of absolute differences between two iterations is below <code>tol</code>.</dd>


<dt>verbose</dt>
<dd>if TRUE (default) the output will be printed.</dd>


<dt>initial_point</dt>
<dd>if TRUE (default), the algorithm will construct an initial point before the iteration steps.</dd>

</dl>

emtp2: Performs Gaussian likelihood optimization under Laplacian matrix constraints.

Description

Usage

Value

Arguments

References

See Also