.rPexstud

Generate from extremal Student-t \(Y \sim {P_x}\), where
\(P_{x}\) is probability of extremal function

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

.rPexstud function

<dl><dt>index</dt>
<dd>index of the location. An integer in 0, ..., \(d-1\)</dd>
<dt>cholesky</dt>
<dd>Cholesky root of transformed correlation matrix</dd>
<dt>sigma</dt>
<dd>a positive semi-definite correlation matrix</dd>
<dt>al</dt>
<dd>the alpha parameter in Proposition 7. Corresponds to degrees of freedom - 1</dd></dl>

Arguments

Generate from extremal Student-t \(Y \sim {P_x}\), where
\(P_{x}\) is probability of extremal function — .rPexstud

<dl>

<dt>index</dt>
<dd>index of the location. An integer in 0, ..., \(d-1\)</dd>


<dt>cholesky</dt>
<dd>Cholesky root of transformed correlation matrix</dd>


<dt>sigma</dt>
<dd>a positive semi-definite correlation matrix</dd>


<dt>al</dt>
<dd>the alpha parameter in Proposition 7. Corresponds to degrees of freedom - 1</dd>

</dl>

.rPexstud: Generate from extremal Student-t \(Y \sim {P_x}\), where \(P_{x}\) is probability of extremal function

Description

Usage

Value

Arguments