gevr.temstat

gevr.Vfun

gevr.phi

gevr.dphi

Vector implementing conditioning on approximate ancillary statistics for the TEM

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

gevr.temstat function

<dl><dt>par</dt>
<dd>vector of <code>retlev</code>, <code>scale</code> and <code>shape</code></dd>
<dt>dat</dt>
<dd>sample vector</dd>
<dt>p</dt>
<dd>tail probability, corresponding to \((1-p)\)th quantile for \(z\)</dd>
<dt>V</dt>
<dd>vector calculated by <code>gevr.Vfun</code></dd></dl>

Arguments

Tangent exponential model statistics for the GEV distribution (return level) — gevr.temstat

<dl>

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


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


<dt>p</dt>
<dd>tail probability, corresponding to \((1-p)\)th quantile for \(z\)</dd>


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

</dl>

Tangent exponential model statistics for the GEV distribution (return level)

gevr.temstat: Tangent exponential model statistics for the GEV distribution (return level)

Description

Usage

Arguments

See Also