gpdr {mev} | R Documentation |
Generalized Pareto distribution (return level parametrization)
Description
Likelihood, score function and information matrix, approximate ancillary statistics and sample space derivative for the generalized Pareto distribution parametrized in terms of return levels.
Arguments
par |
vector of length 2 containing |
dat |
sample vector |
m |
number of observations of interest for return levels. See Details |
tol |
numerical tolerance for the exponential model |
method |
string indicating whether to use the expected ( |
nobs |
number of observations |
V |
vector calculated by |
Details
The observed information matrix was calculated from the Hessian using symbolic calculus in Sage.
The interpretation for m
is as follows: if there are on average m_y
observations per year above the threshold, then m=Tm_y
corresponds to T
-year return level.
Usage
gpdr.ll(par, dat, m, tol=1e-5) gpdr.ll.optim(par, dat, m, tol=1e-5) gpdr.score(par, dat, m) gpdr.infomat(par, dat, m, method = c('obs', 'exp'), nobs = length(dat)) gpdr.Vfun(par, dat, m) gpdr.phi(par, V, dat, m) gpdr.dphi(par, V, dat, m)
Functions
-
gpdr.ll
: log likelihood -
gpdr.ll.optim
: negative log likelihood parametrized in terms oflog(scale)
and shape in order to perform unconstrained optimization -
gpdr.score
: score vector -
gpdr.infomat
: observed information matrix for GPD parametrized in terms of rate ofm
-year return level and shape -
gpdr.Vfun
: vector implementing conditioning on approximate ancillary statistics for the TEM -
gpdr.phi
: canonical parameter in the local exponential family approximation -
gpdr.dphi
: derivative matrix of the canonical parameter in the local exponential family approximation
Author(s)
Leo Belzile