evmc: Simulate Markov Chains With Extreme Value Dependence...

In POT: Generalized Pareto Distribution and Peaks Over Threshold

Simulate Markov Chains With Extreme Value Dependence Structures

Description

Simulation of first order Markov chains, such that each pair of consecutive values has the dependence structure of one of nine parametric bivariate extreme value distributions.

Usage

simmc(n, alpha, model = "log", asCoef, asCoef1, asCoef2, margins =
"uniform")

Arguments

n	Number of observations.
alpha	Dependence parameter for the logistic, asymmetric logistic, negative logistic, asymmetric negative logistic, mixed and asymmetric mixed models.
asCoef,asCoef1,asCoef2	The asymmetric coefficients for the asymmetric logistic, asymmetric negative logistic and asymmetric mixed models.
model	The specified model; a character string. Must be either "log" (the default), "alog", "nlog", "anlog", "mix" or "amix", for the logistic, asymmetric logistic, negative logistic, asymmetric negative logistic, mixed and asymmetric mixed models respectively.
margins	The marginal distribution of each value; a character string. Must be either "uniform" (the default), "rweibull", "frechet" or "gumbel", for the uniform, standard reversed Weibull, standard Gumbel and standard Frechet distributions respectively.

Value

A numeric vector of length n.

Author(s)

Alec Stephenson (modified for the POT package by Mathieu Ribatet)

Examples

simmc(100, alpha = 0.1, model = "log")
simmc(100, alpha = 1.2, model = "nlog", margins = "gum")

POT documentation built on April 14, 2022, 3:03 a.m.