Estimator of small tail probability in regression
Estimator of small tail probability 1-F_i(q) in the regression case where γ is constant and the regression modelling is thus only solely placed on the scale parameter.
ProbReg(Z, A, q, plot = FALSE, add = FALSE, main = "Estimates of small exceedance probability", ...)
Z |
Vector of n observations (from the response variable). |
A |
Vector of n-1 estimates for A(i/n) obtained from |
q |
The used large quantile (we estimate P(X_i>q)) for q large). |
plot |
Logical indicating if the estimates should be plotted as a function of k, default is |
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 estimator is defined as
1-\hat{F}_i(q) = \hat{A}(i/n) (k+1)/(n+1) (q/Z_{n-k,n})^{-1/H_{k,n}},
with H_{k,n} the Hill estimator. Here, it is assumed that we have equidistant covariates x_i=i/n.
See Section 4.4.1 in Albrecher et al. (2017) for more details.
A list with following components:
k |
Vector of the values of the tail parameter k. |
P |
Vector of the corresponding probability estimates. |
q |
The used large quantile. |
Tom Reynkens.
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
data(norwegianfire) Z <- norwegianfire$size[norwegianfire$year==76] i <- 100 n <- length(Z) # Scale estimator in i/n A <- ScaleReg(i/n, Z, h=0.5, kernel = "epanechnikov")$A # Small exceedance probability q <- 10^6 ProbReg(Z, A, q, plot=TRUE) # Large quantile p <- 10^(-5) QuantReg(Z, A, p, plot=TRUE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.