thrselect {ercv} | R Documentation |
Threshold selection algorithm
Description
Threshold selection algorithm.
Usage
thrselect(data, threshold=NA, nextremes=NA, omit=16, evi=NA, m=10, nsim=100,
conf.level=0.90, oprint=TRUE)
Arguments
data |
a numeric vector. |
threshold |
a threshold value (either this or |
nextremes |
the number of upper extremes to be used (either
this or |
omit |
the minimum required number of upper extremes for computing residual statistics. |
evi |
extreme value index. In particular, the shape parammeter of a generalized Pareto distribution. |
m |
number of thresholds to do multiplicial test. |
nsim |
number of simulations. |
conf.level |
confidence level of the interval. |
oprint |
logical. If |
Value
A list including two data.frame
(solution and options). Each of the data.frame
contains the following columns:
m number of thresholds for testing tail index.
nextremes number of thresholds for testing tail index.
threshold the threshold value
rcv residual coefficient of variation for selected threshold.
cvopt optimal coefficient of variation for the tail.
evi the corresponding tail index for optimal coefficient of variation if
evi
parameter isNA
.tms the statistic of the tail index test.
pvalue p-value associated to
tms
.
Author(s)
Joan del Castillo, David MoriƱa Soler and Isabel Serra
References
del Castillo, J. and Padilla, M. (2016). Modeling extreme values by the residual coefficient of variation. SORT Statist. Oper. Res. Trans. 40(2), 303-320.
del Castillo, J. and Serra, I. (2015). Likelihood inference for Generalized Pareto Distribution. Computational Statistics and Data Analysis, 83, 116-128.
del Castillo, J., Daoudi, J. and Lockhart, R. (2014). Methods to Distinguish Between Polynomial and Exponential Tails. Scandinavian Journal of Statistics, 41, 382-393.
See Also
ercv-package
, cievi
,
ccdfplot
, cvevi
, cvplot
, evicv
, fitpot
,
ppot
, qpot
, tdata
,
Tm
Examples
data("nidd.thresh", package = "evir")
thrselect(nidd.thresh, nsim=500)