falk {smoothtail} | R Documentation |
Given an ordered sample of either exceedances or upper order statistics which is to be modeled using a GPD, this function provides Falk's estimator of the shape parameter gamma in [-1,0]. Precisely,
hat gamma_{rm{Falk}} = hat gamma_{rm{Falk}}(k, n) = frac{1}{k-1} sum_{j=2}^k log Bigl(frac{X_{(n)}-H^{-1}((n-j+1)/n)}{X_{(n)}-H^{-1}((n-k)/n)} Bigr), ; ; k=3, ... ,n-1
for $H$ either the empirical or the distribution function based on the log–concave density estimator. Note that for any k, hat gamma_{rm{Falk}} : R^n to (-infty, 0). If hat gamma_{rm{Falk}} not in [-1,0), then it is likely that the log-concavity assumption is violated.
falk(x)
x |
Sample of strictly increasing observations. |
n x 3 matrix with columns: indices k, Falk's estimator using the smoothing method, and the ordinary Falk's estimator based on the order statistics.
Kaspar Rufibach (maintainer), kaspar.rufibach@gmail.com
Samuel Mueller, mueller@maths.uwa.edu.au,
http://www.maths.usyd.edu.au/ut/people?who=S_Mueller
Kaspar Rufibach acknowledges support by the Swiss National Science Foundation SNF, http://www.snf.ch
Mueller, S. and Rufibach K. (2006). Smooth tail index estimation. J. Stat. Comput. Simul., to appear.
Falk, M. (1995). Some best parameter estimates for distributions with finite endpoint. Statistics, 27, 115–125.
Other approaches to estimate gamma based on the fact that the density is log–concave, thus
gamma in [-1,0], are available as the functions pickands
, falkMVUE
, generalizedPick
.
# generate ordered random sample from GPD set.seed(1977) n <- 20 gam <- -0.75 x <- rgpd(n, gam) # compute tail index estimators falk(x)