pp: Poisson process of extremes.
In mev: Modelling of Extreme Values

pp	R Documentation

Poisson process of extremes.

Description

Likelihood, score function and information matrix for the Poisson process likelihood.

Arguments

`par`	vector of `loc`, `scale` and `shape`
`dat`	sample vector
`u`	threshold
`method`	string indicating whether to use the expected (`'exp'`) or the observed (`'obs'` - the default) information matrix.
`np`	number of periods of observations. This is a post hoc adjustment for the intensity so that the parameters of the model coincide with those of a generalized extreme value distribution with block size `length(dat)/np`.
`nobs`	number of observations for the expected information matrix. Default to `length(dat)` if `dat` is provided.

Usage

pp.ll(par, dat)
pp.ll(par, dat, u, np)
pp.score(par, dat)
pp.infomat(par, dat, method = c('obs', 'exp'))

Functions

pp.ll: log likelihood
pp.score: score vector
pp.infomat: observed or expected information matrix

Author(s)

Leo Belzile

References

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, 209 p.

Wadsworth, J.L. (2016). Exploiting Structure of Maximum Likelihood Estimators for Extreme Value Threshold Selection, Technometrics, 58(1), 116-126, http://dx.doi.org/10.1080/00401706.2014.998345.

Sharkey, P. and J.A. Tawn (2017). A Poisson process reparameterisation for Bayesian inference for extremes, Extremes, 20(2), 239-263, http://dx.doi.org/10.1007/s10687-016-0280-2.

mev documentation built on April 20, 2023, 5:10 p.m.