gpdr {mev} | R Documentation |
Likelihood, score function and information matrix, approximate ancillary statistics and sample space derivative for the generalized Pareto distribution parametrized in terms of return levels.
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 |
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.
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)
gpdr.ll
: log likelihood
gpdr.ll.optim
: negative log likelihood parametrized in terms of log(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 of m
-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
Leo Belzile