Likelihood, score function and information matrix for the Poisson process likelihood.
vector of loc
, scale
and shape
sample vector
threshold
string indicating whether to use the expected ('exp'
) or the observed ('obs'
- the default) information matrix.
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
.
number of observations for the expected information matrix. Default to length(dat)
if dat
is provided.
pp.ll(par, dat)
pp.ll(par, dat, u, np)
pp.score(par, dat)
pp.infomat(par, dat, method = c('obs', 'exp'))
pp.ll
: log likelihood
pp.score
: score vector
pp.infomat
: observed or expected information matrix
Leo Belzile
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
.