ProbGPD {ReIns} | R Documentation |
Estimator of small exceedance probabilities and large return periods using GPD-MLE
Description
Computes estimates of a small exceedance probability P(X>q)
or large return period 1/P(X>q)
using the GPD fit for the peaks over a threshold.
Usage
ProbGPD(data, gamma, sigma, q, plot = FALSE, add = FALSE,
main = "Estimates of small exceedance probability", ...)
ReturnGPD(data, gamma, sigma, q, plot = FALSE, add = FALSE,
main = "Estimates of large return period", ...)
Arguments
data |
Vector of |
gamma |
Vector of |
sigma |
Vector of |
q |
The used large quantile (we estimate |
plot |
Logical indicating if the estimates should be plotted as a function of |
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 |
Details
See Section 4.2.2 in Albrecher et al. (2017) for more details.
Value
A list with following components:
k |
Vector of the values of the tail parameter |
P |
Vector of the corresponding probability estimates, only returned for |
R |
Vector of the corresponding estimates for the return period, only returned for |
q |
The used large quantile. |
Author(s)
Tom Reynkens.
References
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.
See Also
Examples
data(soa)
# Look at last 500 observations of SOA data
SOAdata <- sort(soa$size)[length(soa$size)-(0:499)]
# GPD-ML estimator
pot <- GPDmle(SOAdata)
# Exceedance probability
q <- 10^7
ProbGPD(SOAdata, gamma=pot$gamma, sigma=pot$sigma, q=q, plot=TRUE)
# Return period
q <- 10^7
ReturnGPD(SOAdata, gamma=pot$gamma, sigma=pot$sigma, q=q, plot=TRUE)