Estimator of large quantiles using censored Hill
Computes estimates of large quantiles Q(1-p) using the estimates for the EVI obtained from the Hill estimator adapted for right censoring.
cQuant(data, censored, gamma1, p, plot = FALSE, add = FALSE, main = "Estimates of extreme quantile", ...)
data |
Vector of n observations. |
censored |
A logical vector of length n indicating if an observation is censored. |
gamma1 |
Vector of n-1 estimates for the EVI obtained from |
p |
The exceedance probability of the quantile (we estimate Q(1-p) for p small). |
plot |
Logical indicating if the estimates should be plotted as a function of k, default is |
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 |
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}.
A list with following components:
k |
Vector of the values of the tail parameter k. |
Q |
Vector of the corresponding quantile estimates. |
p |
The used exceedance probability. |
Tom Reynkens.
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.
# 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)
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