rho.ghp

Estimator of the second order regular variation parameter \(\rho \leq 0\) parameter for heavy-tailed data proposed by Gomes et al. (2003)

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

Raphael Huser

Scott D. Grimshaw

Jin Zhang

Michael A. Stephens

Art B. Owen

rho.ghp function

<dl><dt>xdat</dt>
<dd>vector of positive observations</dd>
<dt>k</dt>
<dd>number of highest order statistics to use for estimation</dd>
<dt>alpha</dt>
<dd>positive scalar tuning parameter</dd></dl>

Arguments

Second order tail index estimator of Gomes et al. — rho.ghp

<dl>

<dt>xdat</dt>
<dd>vector of positive observations</dd>


<dt>k</dt>
<dd>number of highest order statistics to use for estimation</dd>


<dt>alpha</dt>
<dd>positive scalar tuning parameter</dd>

</dl>

rho.ghp: Second order tail index estimator of Gomes et al.

Description

Usage

Arguments

References