Multiple threshold test for a GPD.
Tm(data, threshold = NA, nextremes = NA, omit = 16, evi = NA, m = 10, nsim = 100)
a numeric vector.
a threshold value (either this or nextremes
must
be given but not both).
the number of upper extremes to be used (either
this or threshold
must be given but not both).
the minimum required number of upper extremes for computing residual statistics.
extreme value index. In particular, the shape parammeter of a generalized Pareto distribution.
number of thresholds to do multiplicial test.
number of simulations.
A data.frame
containing the following columns:
nextremes the number of upper extremes to be used.
cvopt optimal coefficient of variation for the tail.
evi
the corresponding tail index for optimal coefficient of variation if evi
parameter is NA
.
tms the statistic of the tail index test.
pvalue
p-value associated to tms
.
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.
ercv-package
, cievi
,
ccdfplot
, cvevi
, cvplot
, evicv
, fitpot
,
ppot
, qpot
, tdata
, thrselect
# NOT RUN {
data("nidd.thresh",package = "evir")
Tm(nidd.thresh,evi=0, nextremes = 75)
# }
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