Hill.2oQV | R Documentation |
Computes bias-reduced ML estimates of gamma based on the quantile view.
Hill.2oQV(data, start = c(1,1,1), warnings = FALSE, logk = FALSE,
plot = FALSE, add = FALSE, main = "Estimates of the EVI", ...)
data |
Vector of |
start |
A vector of length 3 containing starting values for the first numerical optimisation (see Details). The elements
are the starting values for the estimators of |
warnings |
Logical indicating if possible warnings from the optimisation function are shown, default is |
logk |
Logical indicating if the estimates are plotted as a function of |
plot |
Logical indicating if the estimates of |
add |
Logical indicating if the estimates of |
main |
Title for the plot, default is |
... |
Additional arguments for the |
See Section 4.2.1 of Albrecher et al. (2017) for more details.
A list with following components:
k |
Vector of the values of the tail parameter |
gamma |
Vector of the ML estimates for the EVI for each value of |
b |
Vector of the ML estimates for the parameter |
beta |
Vector of the ML estimates for the parameter |
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., Dierckx, G., Goegebeur Y. and Matthys, G. (1999). "Tail Index Estimation and an Exponential Regression Model." Extremes, 2, 177–200.
Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.
data(norwegianfire)
# Plot bias-reduced MLE (QV) as a function of k
Hill.2oQV(norwegianfire$size[norwegianfire$year==76],plot=TRUE)
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