rlarg {mev}

Distribution of the r-largest observations

Description

Likelihood, score function and information matrix for the r-largest observations likelihood.

Arguments

par

vector of loc, scale and shape

dat

an n by r sample matrix, ordered from largest to smallest in each row

method

string indicating whether to use the expected ('exp') or the observed ('obs' - the default) information matrix.

nobs

number of observations for the expected information matrix. Default to nrow(dat) if dat is provided.

r

number of order statistics kept. Default to ncol(dat)

Usage

rlarg.ll(par, dat, u, np)
rlarg.score(par, dat)
rlarg.infomat(par, dat, method = c('obs', 'exp'), nobs = nrow(dat), r = ncol(dat))

Functions

• rlarg.ll: log likelihood

• rlarg.score: score vector

• rlarg.infomat: observed or expected information matrix

Author(s)

Leo Belzile

References

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, 209 p.

Smith, R.L. (1986). Extreme value theory based on the r largest annual events, Journal of Hydrology, 86(1-2), 27–43, http://dx.doi.org/10.1016/0022-1694(86)90004-1.