excess.l: Likelihood for vectors of exceedance with censored components

In HiDimMaxStable: Inference on High Dimensional Max-Stable Distributions

Description

Computes the likelihood for observations of vectors of exceedances that belong to the maximum domain of attraction of a multivariate max-stable distribution whose spectral random vector is Gaussian, Log-normal or has a clustered copula distribution.

Usage

excess.l(data,ln=FALSE,...)

Arguments

data	a matrix representing the data. Each column corresponds to one observation of a vector of exceedance with censored components. Note that all components must be larger or equal to one.
ln	logical. If TRUE log-density is computed.
...	further arguments to be passed to mubz.* function (where * stands for the category of the model). In particular, category is a character string indicating the model to be used: "normal", "lnormal" or "copula", and params gives the values of the parameters for which the likelihood is computed.

See Also

mubz.normal,mubz.lnormal, mubz.copula.

Examples

raw.data<-rCMS(copulas=c(copClayton,copGumbel),
               margins=c(marginLnorm,marginFrechet),
               classes=c(rep(1,4),rep(2,4)),
               params=c(0.5,1,1.5,1.7),n=50)
data<-excess.censor(raw.data)


d<-excess.l(data,params=c(0.5,1,1.5,1.7),
            category="copula",
            copulas=c(copClayton,copGumbel),
            margins=c(marginLnorm,marginFrechet),
            classes=c(rep(1,4),rep(2,4)))

HiDimMaxStable documentation built on May 29, 2017, 6:20 p.m.