bgev 0.3
Estimation
bgev_mle() was rewritten. It now uses a multistart
Nelder-Mead search on a reparametrised scale (log(sigma),
log(delta)), seeded by quantile matching plus loose
data-driven box starts. Estimation is restricted to
delta > 0 (bimodality requires it, and for
delta < 0 the likelihood is unbounded at
x = mu); the distribution functions still accept the full
delta > -1.- The return value changed: instead of the old
DEoptim
object ($optim$bestmem), bgev_mle() now
returns $par, $se, $loglik and
diagnostics. This is a breaking change.
- Standard errors (
se) are returned from the inverse
observed-information Hessian, for an admissible (regular) optimum only;
near the parameter-dependent support boundary they are not reliable and
are returned as NA.
- New diagnostics on every fit:
convergence,
agree, admissible (a positive-definite-Hessian
acceptance gate that rejects spurious optima), boundary,
and optimum.
likelihood = "grouped_likelihood" added for discrete or
rounded data, using the interval likelihood
F(x + h/2) - F(x - h/2); tied data under the continuous
density now raises a warning.- New
bgev_profile_likelihood() for the profile
log-likelihood of a parameter.
Documentation
- New vignette “Maximum Likelihood Estimation for the BGEV
Distribution”, including a Monte Carlo validation of the estimator.
Internal
distCheck() renamed to dist_check().- Imports: dropped
DEoptim and lhs; added
numDeriv and graphics.