gev.bias

Bias vector for the GEV distribution based on an <code>n</code> sample.
Due to numerical instability, values of the information matrix and the bias
are linearly interpolated when the value of the shape parameter is close to zero.

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

gev.bias function

<dl><dt>par</dt>
<dd>vector of <code>loc</code>, <code>scale</code> and <code>shape</code></dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Cox-Snell first order bias for the GEV distribution — gev.bias

<dl>

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


<dt>n</dt>
<dd>sample size</dd>

</dl>

gev.bias: Cox-Snell first order bias for the GEV distribution

Description

Usage

Arguments

See Also