Likelihood, score function and information matrix, bias, approximate ancillary statistics and sample space derivative for the generalized Pareto distribution
Usage
gpd.ll(par, dat, tol=1e-5)
gpd.ll.optim(par, dat, tol=1e-5)
gpd.score(par, dat)
gpd.infomat(par, dat, method = c('obs','exp'))
gpd.bias(par, n)
gpd.Fscore(par, dat, method = c('obs','exp'))
gpd.Vfun(par, dat)
gpd.phi(par, dat, V)
gpd.dphi(par, dat, V)
Functions
gpd.ll
: log likelihoodgpd.ll.optim
: negative log likelihood parametrized in terms oflog(scale)
and shape in order to perform unconstrained optimizationgpd.score
: score vectorgpd.infomat
: observed or expected information matrixgpd.bias
: Cox-Snell first order biasgpd.Fscore
: Firth's modified score equationgpd.Vfun
: vector implementing conditioning on approximate ancillary statistics for the TEMgpd.phi
: canonical parameter in the local exponential family approximationgpd.dphi
: derivative matrix of the canonical parameter in the local exponential family approximation
References
Firth, D. (1993). Bias reduction of maximum likelihood estimates, Biometrika, 80(1), 27–38.
Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, 209 p.
Cox, D. R. and E. J. Snell (1968). A general definition of residuals, Journal of the Royal Statistical Society: Series B (Methodological), 30, 248–275.
Cordeiro, G. M. and R. Klein (1994). Bias correction in ARMA models, Statistics and Probability Letters, 19(3), 169–176.
Giles, D. E., Feng, H. and R. T. Godwin (2016). Bias-corrected maximum likelihood estimation of the parameters of the generalized Pareto distribution, Communications in Statistics - Theory and Methods, 45(8), 2465–2483.