EPDfit: Fit EPD using MLE

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

Fit EPD using MLE

Description

Fit the Extended Pareto Distribution (EPD) to data using Maximum Likelihood Estimation (MLE).

Usage

EPDfit(data, tau, start = c(0.1, 1), warnings = FALSE)

Arguments

data	Vector of n observations.
tau	Value for the \tau parameter of the EPD.
start	Vector of length 2 containing the starting values for the optimisation. The first element is the starting value for the estimator of \gamma and the second element is the starting value for the estimator of \kappa. Default is c(0.1,1).
warnings	Logical indicating if possible warnings from the optimisation function are shown, default is FALSE.

Details

See Section 4.2.1 of Albrecher et al. (2017) for more details.

Value

A vector with the MLE estimate for the \gamma parameter of the EPD as the first component and the MLE estimate for the \kappa parameter of the EPD as the second component.

Author(s)

Tom Reynkens

References

Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.

Beirlant, J., Joossens, E. and Segers, J. (2009). "Second-Order Refined Peaks-Over-Threshold Modelling for Heavy-Tailed Distributions." Journal of Statistical Planning and Inference, 139, 2800–2815.

See Also

EPD, GPDfit

Examples

data(soa)

# Look at last 500 observations of SOA data
SOAdata <- sort(soa$size)[length(soa$size)-(0:499)]

# Fit EPD to last 500 observations
res <- EPDfit(SOAdata/sort(soa$size)[500], tau=-1)

ReIns documentation built on Nov. 3, 2023, 5:08 p.m.