intensXstud

The intensity function includes the normalizing constants

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

Raphael Huser

Scott D. Grimshaw

Jin Zhang

Michael A. Stephens

Art B. Owen

intensXstud function

<dl><dt>tdat</dt>
<dd>matrix of unit Pareto observations</dd>
<dt>df</dt>
<dd>degrees of freedom, must be larger than 1</dd>
<dt>Sigma</dt>
<dd>scale matrix</dd>
<dt>cholPrecis</dt>
<dd>Cholesky root of the precision matrix <code>solve(Sigma)</code>. Default to <code>NULL</code>, meaning the latter is calculated within the function</dd></dl>

Arguments

Intensity function for the extremal Student model — intensXstud

<dl>

<dt>tdat</dt>
<dd>matrix of unit Pareto observations</dd>


<dt>df</dt>
<dd>degrees of freedom, must be larger than 1</dd>


<dt>Sigma</dt>
<dd>scale matrix</dd>


<dt>cholPrecis</dt>
<dd>Cholesky root of the precision matrix <code>solve(Sigma)</code>. Default to <code>NULL</code>, meaning the latter is calculated within the function</dd>

</dl>

intensXstud: Intensity function for the extremal Student model

Description

Usage

Value

Arguments