Estimator of large quantiles using censored MOM
Computes estimates of large quantiles Q(1-p) using the estimates for the EVI obtained from the MOM estimator adapted for right censoring.
cQuantMOM(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} + a_{k,n} ( ( (1-km)/p)^{\hat{γ}_1} -1 ) / \hat{γ}_1)
ith Z_{i,n} the i-th order statistic of the data, \hat{γ}_1 the MOM estimator adapted for right censoring and km the Kaplan-Meier estimator for the CDF evaluated in Z_{n-k,n}. The value a is defined as
a_{k,n} = Z_{n-k,n} H_{k,n} (1-\min(\hat{γ}_1,0)) /\hat{p}_k
with H_{k,n} the ordinary Hill estimator and \hat{p}_k the proportion of the k largest observations that is non-censored.
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
Einmahl, J.H.J., Fils-Villetard, A. and Guillou, A. (2008). "Statistics of Extremes Under Random Censoring." Bernoulli, 14, 207–227.
# 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) # Moment estimator adapted for right censoring cmom <- cMoment(Z, censored=censored, plot=TRUE) # Large quantile p <- 10^(-4) cQuantMOM(Z, censored=censored, gamma1=cmom$gamma1, p=p, plot=TRUE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.