cGPD: GPD-ML estimator for right censored data

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Computes ML estimates of fitting GPD to peaks over a threshold adapted for right censoring.

Usage

1
2
3
4
5
cGPDmle(data, censored, start = c(0.1,1), warnings = FALSE, logk = FALSE, 
        plot = FALSE, add = FALSE, main = "POT estimates of the EVI", ...)
      
cPOT(data, censored, start = c(0.1,1), warnings = FALSE, logk = FALSE, 
     plot = FALSE, add = FALSE, main = "POT estimates of the EVI", ...)

Arguments

data

Vector of n observations.

censored

A logical vector of length n indicating if an observation is censored.

start

Vector of length 2 containing the starting values for the optimisation. The first element is the starting value for the estimator of γ_1 and the second element is the starting value for the estimator of σ_1. Default is c(0.1,1).

warnings

Logical indicating if possible warnings from the optimisation function are shown, default is FALSE.

logk

Logical indicating if the estimates are plotted as a function of \log(k) (logk=TRUE) or as a function of k. Default is FALSE.

plot

Logical indicating if the estimates of γ_1 should be plotted as a function of k, default is FALSE.

add

Logical indicating if the estimates of γ_1 should be added to an existing plot, default is FALSE.

main

Title for the plot, default is "POT estimates of the EVI".

...

Additional arguments for the plot function, see plot for more details.

Details

The GPD-MLE estimator for the EVI adapted for right censored data is equal to the ordinary GPD-MLE estimator for the EVI divided by the proportion of the k largest observations that is non-censored. The estimates for σ are the ordinary GPD-MLE estimates for σ.

This estimator is only suitable for right censored data.

cPOT is the same function but with a different name for compatibility with POT.

Value

A list with following components:

k

Vector of the values of the tail parameter k.

gamma1

Vector of the corresponding MLE estimates for the γ_1 parameter of the GPD.

sigma1

Vector of the corresponding MLE estimates for the σ_1 parameter of the GPD.

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

GPDmle, cProbGPD, cQuantGPD, cEPD

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
# 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-ML estimator adapted for right censoring
cpot <- cGPDmle(Z, censored=censored, plot=TRUE)

ReIns documentation built on July 2, 2020, 4:03 a.m.