Fit GPD using MLE
Fit the Generalised Pareto Distribution (GPD) to data using Maximum Likelihood Estimation (MLE).
GPDfit(data, start = c(0.1, 1), warnings = FALSE)
data |
Vector of n observations. |
start |
Vector of length 2 containing the starting values for the optimisation. The first element
is the starting value for the estimator of γ and the second element is the starting value for the estimator of σ. Default is |
warnings |
Logical indicating if possible warnings from the optimisation function are shown, default is |
See Section 4.2.2 in Albrecher et al. (2017) for more details.
A vector with the MLE estimate for the γ parameter of the GPD as the first component and the MLE estimate for the σ parameter of the GPD as the second component.
Tom Reynkens based on S-Plus code from Yuri Goegebeur and R code from Klaus Herrmann.
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)] # Fit GPD to last 500 observations res <- GPDfit(SOAdata-sort(soa$size)[500])
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