trProb | R Documentation |
Computes estimates of a small exceedance probability P(X>q)
using the estimates for the EVI obtained from the Hill estimator adapted for upper truncation.
trProb(data, r = 1, gamma, q, warnings = TRUE, plot = FALSE, add = FALSE,
main = "Estimates of small exceedance probability", ...)
data |
Vector of |
r |
Trimming parameter, default is |
gamma |
Vector of |
q |
The used large quantile (we estimate |
warnings |
Logical indicating if warnings are shown, default is |
plot |
Logical indicating if the estimates should be plotted as a function of |
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 |
The probability is estimated as
\hat{P}(X>q)=(k+1)/(n+1) ( (q/X_{n-k,n})^{-1/\gamma_k} - R_{r,k,n}^{1/\hat{\gamma}_k} ) / (1- R_{r,k,n}^{1/\hat{\gamma}_k})
with R_{r,k,n} = X_{n-k,n} / X_{n-r+1,n}
and \hat{\gamma}_k
the Hill estimates adapted for truncation.
See Beirlant et al. (2016) or Section 4.2.3 of Albrecher et al. (2017) for more details.
A list with following components:
k |
Vector of the values of the tail parameter |
P |
Vector of the corresponding probability estimates. |
q |
The used large quantile. |
Tom Reynkens based on R
code of Dries Cornilly.
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
Beirlant, J., Fraga Alves, M.I. and Gomes, M.I. (2016). "Tail fitting for Truncated and Non-truncated Pareto-type Distributions." Extremes, 19, 429–462.
trHill
, trQuant
, Prob
, trProbMLE
# Sample from truncated Pareto distribution.
# truncated at 99% quantile
shape <- 2
X <- rtpareto(n=1000, shape=shape, endpoint=qpareto(0.99, shape=shape))
# Truncated Hill estimator
trh <- trHill(X, plot=TRUE, ylim=c(0,2))
# Small probability
trProb(X, gamma=trh$gamma, q=8, plot=TRUE)
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