cQuant {ReIns} | R Documentation |
Estimator of large quantiles using censored Hill
Description
Computes estimates of large quantiles Q(1-p)
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
\hat{Q}(1-p)=Z_{n-k,n} \times ( (1-km)/p)^{H_{k,n}^c}
with Z_{i,n}
the i
-th order statistic of the data, H_{k,n}^c
the Hill estimator adapted for right censoring and km
the Kaplan-Meier estimator for the CDF evaluated in Z_{n-k,n}
.
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)