gevN {mev} | R Documentation |
Likelihood, score function and information matrix,
approximate ancillary statistics and sample space derivative
for the generalized extreme value distribution parametrized in terms of the
quantiles/mean of N-block maxima parametrization z
, scale and shape.
par |
vector of |
dat |
sample vector |
V |
vector calculated by |
q |
probability, corresponding to |
qty |
string indicating whether to calculate the |
gevN.ll(par, dat, N, q, qty = c('mean', 'quantile')) gevN.ll.optim(par, dat, N, q = 0.5, qty = c('mean', 'quantile')) gevN.score(par, dat, N, q = 0.5, qty = c('mean', 'quantile')) gevN.infomat(par, dat, qty = c('mean', 'quantile'), method = c('obs', 'exp'), N, q = 0.5, nobs = length(dat)) gevN.Vfun(par, dat, N, q = 0.5, qty = c('mean', 'quantile')) gevN.phi(par, dat, N, q = 0.5, qty = c('mean', 'quantile'), V) gevN.dphi(par, dat, N, q = 0.5, qty = c('mean', 'quantile'), V)
gevN.ll
: log likelihood
gevN.score
: score vector
gevN.infomat
: expected and observed information matrix
gevN.Vfun
: vector implementing conditioning on approximate ancillary statistics for the TEM
gevN.phi
: canonical parameter in the local exponential family approximation
gevN.dphi
: derivative matrix of the canonical parameter in the local exponential family approximation
Leo Belzile