gevr: Generalized extreme value distribution (return level...

In mev: Modelling of Extreme Values

Generalized extreme value distribution (return level parametrization)

Description

Likelihood, score function and information matrix, approximate ancillary statistics and sample space derivative for the generalized extreme value distribution parametrized in terms of the return level z, scale and shape.

Arguments

par	vector of retlev, scale and shape
dat	sample vector
p	tail probability, corresponding to (1-p)th quantile for z
method	string indicating whether to use the expected ('exp') or the observed ('obs' - the default) information matrix.
nobs	number of observations
V	vector calculated by gevr.Vfun

Usage

gevr.ll(par, dat, p)
gevr.ll.optim(par, dat, p)
gevr.score(par, dat, p)
gevr.infomat(par, dat, p, method = c('obs', 'exp'), nobs = length(dat))
gevr.Vfun(par, dat, p)
gevr.phi(par, dat, p, V)
gevr.dphi(par, dat, p, V)

Functions

• gevr.ll: log likelihood

• gevr.ll.optim: negative log likelihood parametrized in terms of return levels, log(scale) and shape in order to perform unconstrained optimization

• gevr.score: score vector

• gevr.infomat: observed information matrix

• gevr.Vfun: vector implementing conditioning on approximate ancillary statistics for the TEM

• gevr.phi: canonical parameter in the local exponential family approximation

• gevr.dphi: derivative matrix of the canonical parameter in the local exponential family approximation

Author(s)

Leo Belzile

mev documentation built on Sept. 11, 2024, 8:14 p.m.