fitMixing: Maximum Likelihood Estimation

Description Usage Arguments Details Value Author(s) References Examples

View source: R/fitMixing.R

Description

Maximum Likelihood Estimation for the dynamic weighted mixture model of Frigessi et al., 2002

Usage

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fitMixing(cell, body, tail, method="L-BFGS-B", c_location0=0.75, c_scale0=2)

Arguments

cell

lossdat cell

body

body distribution, either gamma, lnorm or weibull

tail

tail distribution, either gamma, lnorm, weibull or gpd

method

optimization method, default is "L-BFGS-B"

c_location0

empirical quantile of loss severity data used for initialization of Cauchy location parameter in optimization: quantile(cell$Loss,c_location0)

c_scale0

scaling factor for empirical standard deviation used for initialization of Cauchy scale parameter in optimization: sd(cell$Loss)/c_scale0

Details

Body and tail parameters are initialized by method of moments estimators. Cauchy location is initialized by empirical 70

Value

Returns a sevdist object of type 'mixing' with the given body and tail distributions fitted to the loss data.

Author(s)

Marius Pfeuffer

References

Frigessi et al. A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection, Extremes 5(3):219-235, 2003

Examples

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## Not run: 
data(lossdat)
sev=fitMixing(lossdat[[1]],"weibull","gpd")
sev
plot(sev,5000)

## End(Not run)

OpVaR documentation built on May 29, 2018, 9:04 a.m.