gpd.temstat

gpd.Vfun

gpd.phi

gpd.dphi

Matrix of approximate ancillary statistics, sample space derivative of the
log likelihood and mixed derivative for the generalized Pareto distribution.

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.temstat function

<dl><dt>par</dt>
<dd>vector of <code>scale</code> and <code>shape</code></dd>
<dt>dat</dt>
<dd>sample vector</dd>
<dt>V</dt>
<dd>vector calculated by <code>gpd.Vfun</code></dd></dl>

Arguments

Tangent exponential model statistics for the generalized Pareto distribution — gpd.temstat

<dl>

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


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


<dt>V</dt>
<dd>vector calculated by <code>gpd.Vfun</code></dd>

</dl>

Tangent exponential model statistics for the generalized Pareto distribution

gpd.temstat: Tangent exponential model statistics for the generalized Pareto distribution

Description

Usage

Arguments

See Also