cQuant {ReIns} | R Documentation |
Estimator of large quantiles using censored Hill
Description
Computes estimates of large quantiles using the estimates for the EVI obtained from the Hill estimator adapted for right censoring.
Usage
cQuant(data, censored, gamma1, p, plot = FALSE, add = FALSE,
main = "Estimates of extreme quantile", ...)
Arguments
data |
Vector of |
censored |
A logical vector of length |
gamma1 |
Vector of |
p |
The exceedance probability of the quantile (we estimate |
plot |
Logical indicating if the estimates should be plotted as a function of |
add |
Logical indicating if the estimates should be added to an existing plot, default is |
main |
Title for the plot, default is |
... |
Additional arguments for the |
Details
The quantile is estimated as
with the
-th order statistic of the data,
the Hill estimator adapted for right censoring and
the Kaplan-Meier estimator for the CDF evaluated in
.
Value
A list with following components:
k |
Vector of the values of the tail parameter |
Q |
Vector of the corresponding quantile estimates. |
p |
The used exceedance probability. |
Author(s)
Tom Reynkens.
References
Beirlant, J., Guillou, A., Dierckx, G. and Fils-Villetard, A. (2007). "Estimation of the Extreme Value Index and Extreme Quantiles Under Random Censoring." Extremes, 10, 151–174.
See Also
cHill
, cProb
, Quant
, KaplanMeier
Examples
# Set seed
set.seed(29072016)
# Pareto random sample
X <- rpareto(500, shape=2)
# Censoring variable
Y <- rpareto(500, shape=1)
# Observed sample
Z <- pmin(X, Y)
# Censoring indicator
censored <- (X>Y)
# Hill estimator adapted for right censoring
chill <- cHill(Z, censored=censored, plot=TRUE)
# Large quantile
p <- 10^(-4)
cQuant(Z, gamma1=chill$gamma, censored=censored, p=p, plot=TRUE)