Estimator of small exceedance probabilities and large return periods using GPD-MLE
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.
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", ...)
data |
Vector of n observations. |
gamma |
Vector of n-1 estimates for the EVI obtained from |
sigma |
Vector of n-1 estimates for σ obtained from |
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 |
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 |
See Section 4.2.2 in Albrecher et al. (2017) for more details.
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 |
R |
Vector of the corresponding estimates for the return period, only returned for |
q |
The used large quantile. |
Tom Reynkens.
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.
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)
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