gpd.infomat

The function returns the expected or observed information matrix.

internal

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Leo Belzile

Modelling of Extreme Values

Jennifer L. Wadsworth

Paul J. Northrop

Scott D. Grimshaw

Jin Zhang

Michael A. Stephens

Art B. Owen

Raphael Huser

gpd.infomat function

<dl><dt>par</dt>
<dd>vector of <code>scale</code> and <code>shape</code></dd>
<dt>dat</dt>
<dd>sample vector</dd>
<dt>method</dt>
<dd>string indicating whether to use the expected (<code>'exp'</code>) or the observed (<code>'obs'</code> - the default) information matrix.</dd>
<dt>nobs</dt>
<dd>number of observations</dd></dl>

Arguments

Information matrix for the generalized Pareto distribution — gpd.infomat

<dl>

<dt>par</dt>
<dd>vector of <code>scale</code> and <code>shape</code></dd>


<dt>dat</dt>
<dd>sample vector</dd>


<dt>method</dt>
<dd>string indicating whether to use the expected (<code>'exp'</code>) or the observed (<code>'obs'</code> - the default) information matrix.</dd>


<dt>nobs</dt>
<dd>number of observations</dd>

</dl>

gpd.infomat: Information matrix for the generalized Pareto distribution

Description

Usage

Arguments

See Also