Description Usage Arguments Value Examples
Estimation of the pquantile based on multiple local Hill estimators and Weissman's extrapolation formula. We assume heavytail homogeneity, i.e., all local EVI's are the same.
1  RegioWeissman(x, j = 1, p, k, k.qu = 20, type = "evopt", alpha = 0.05)

x 
Vector or matrix of observations 
j 
The number of the target site, i.e., if 
p 
The probability of interest; should be between 1k_j/n_j and 1, where n_j is the sample length of the jth column. 
k 
Number of relative excesses involved in the estimation of the extreme value
index gamma. If

k.qu 
Tuning parameter for estimation of empirical variance; only needed if 
type 
Choose either 
alpha 
Confidence level for confidence interval. 
List of
est
Point estimate of pquantile of column j
CI
the corresponding alphaconfidence interval
EVI
estimate of the extreme value index
k
tail sample size
p
a probability
u.kn
(nk)th largest observation, where n is the sample length at station j after removing missing values
description
a short description.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  library("evd")
# sample observations of 75 years at one station:
x < rgev(75, 0, 1, 0) # x is a vector
RegioWeissman(x=x, p=0.95)
x2 < c(NA, NA, x[1:60], NA, x[61:75]) # vector of observations with missing values
RegioWeissman(x=x2, p=0.95) # NAs will be removed
# sample observations of 100 years at 4 stations:
set.seed(1053)
x < matrix(rgev(400, 2, 1, 0.3), ncol=4)
RegioWeissman(x=x, p=0.9, j=3)
# With missing values:
x2 < x
x2[sample(100, 12),2] < NA
RegioWeissman(x=x2, p=0.9, j=3)

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