cProbMOM: Estimator of small exceedance probabilities and large return...

In TReynkens/ReIns: Functions from "Reinsurance: Actuarial and Statistical Aspects"

Estimator of small exceedance probabilities and large return periods using censored MOM

Description

Computes estimates of a small exceedance probability P(X>q) or large return period 1/P(X>q) using the Method of Moments estimates for the EVI adapted for right censoring.

Usage

cProbMOM(data, censored, gamma1, q, plot = FALSE, add = FALSE, 
         main = "Estimates of small exceedance probability", ...)

cReturnMOM(data, censored, gamma1, q, plot = FALSE, add = FALSE, 
           main = "Estimates of large return period", ...)        

Arguments

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 cMoment.
q	The used large quantile (we estimate P(X>q) or 1/P(X>q) for q large).
plot	Logical indicating if the estimates should be plotted as a function of k, default is FALSE.
add	Logical indicating if the estimates should be added to an existing plot, default is FALSE.
main	Title for the plot, default is "Estimates of small exceedance probability" for cProbMOM and "Estimates of large return period" for cReturnMOM.
...	Additional arguments for the plot function, see plot for more details.

Details

The probability is estimated as

\hat{P}(X>q)=(1-km) \times (1+ \hat{\gamma}_1/a_{k,n} \times (q-Z_{n-k,n}))^{-1/\hat{\gamma}_1}

with Z_{i,n} the i-th order statistic of the data, \hat{\gamma}_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{\gamma}_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.

Value

A list with following components:

k	Vector of the values of the tail parameter k.
P	Vector of the corresponding probability estimates, only returned for cProbMOM.
R	Vector of the corresponding estimates for the return period, only returned for cReturnMOM.
q	The used large quantile.

Author(s)

Tom Reynkens

References

Einmahl, J.H.J., Fils-Villetard, A. and Guillou, A. (2008). "Statistics of Extremes Under Random Censoring." Bernoulli, 14, 207–227.

See Also

cQuantMOM, cMoment, ProbMOM, Prob, 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)

# Moment estimator adapted for right censoring
cmom <- cMoment(Z, censored=censored, plot=TRUE)

# Small exceedance probability
q <- 10
cProbMOM(Z, censored=censored, gamma1=cmom$gamma1, q=q, plot=TRUE)

# Return period
cReturnMOM(Z, censored=censored, gamma1=cmom$gamma1, q=q, plot=TRUE)

TReynkens/ReIns documentation built on Nov. 9, 2023, 1:29 p.m.