Computes estimates of large quantiles
cQuantGPD(data, censored, gamma1, sigma1, p, plot = FALSE, add = FALSE,
main = "Estimates of extreme quantile", ...)
A list with following components:
Vector of the values of the tail parameter
Vector of the corresponding quantile estimates.
The used exceedance probability.
Vector of
A logical vector of length
Vector of cGPDmle
.
Vector of cGPDmle
.
The exceedance probability of the quantile (we estimate
Logical indicating if the estimates should be plotted as a function of FALSE
.
Logical indicating if the estimates should be added to an existing plot, default is FALSE
.
Title for the plot, default is "Estimates of extreme quantile"
.
Additional arguments for the plot
function, see plot
for more details.
Tom Reynkens
The quantile is estimated as
Einmahl, J.H.J., Fils-Villetard, A. and Guillou, A. (2008). "Statistics of Extremes Under Random Censoring." Bernoulli, 14, 207--227.
cProbGPD
, cGPDmle
, QuantGPD
, Quant
, KaplanMeier
# 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)
# GPD-MLE estimator adapted for right censoring
cpot <- cGPDmle(Z, censored=censored, plot=TRUE)
# Large quantile
p <- 10^(-4)
cQuantGPD(Z, gamma1=cpot$gamma1, sigma1=cpot$sigma1,
censored=censored, p=p, plot=TRUE)
Run the code above in your browser using DataLab