gpd.lmom

Given a sample of exceedances, compute the first four L-moments and use either the first two to obtain the scale and shape (default), or else use L-skewness and L-scale to compute the scale and shape of the generalized Pareto distribution

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

gpd.lmom function

<dl><dt>xdat</dt>
<dd>[numeric] vector of observations</dd>
<dt>thresh</dt>
<dd>[numeric] optional threshold argument</dd>
<dt>sorted</dt>
<dd>[logical] if <code>TRUE</code>, observations are sorted in increasing order</dd>
<dt>Lskew</dt>
<dd>[logical]; if <code>TRUE</code>, shape is obtained from L-skewness rather than first two moments.</dd></dl>

Arguments

Estimation of generalized Pareto parameters via L-moments — gpd.lmom

<dl>

<dt>xdat</dt>
<dd>[numeric] vector of observations</dd>


<dt>thresh</dt>
<dd>[numeric] optional threshold argument</dd>


<dt>sorted</dt>
<dd>[logical] if <code>TRUE</code>, observations are sorted in increasing order</dd>


<dt>Lskew</dt>
<dd>[logical]; if <code>TRUE</code>, shape is obtained from L-skewness rather than first two moments.</dd>

</dl>

gpd.lmom: Estimation of generalized Pareto parameters via L-moments

Description

Usage

Value

Arguments