rlarg: Distribution of the r-largest observations
In mev: Modelling of Extreme Values

rlarg

R Documentation

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.

mev documentation built on April 20, 2023, 5:10 p.m.