ScaleReg | R Documentation |
Estimator of the scale parameter in the regression case where \gamma
is constant and the regression modelling is thus placed solely on the scale parameter.
ScaleReg(s, Z, kernel = c("normal", "uniform", "triangular", "epanechnikov", "biweight"),
h, plot = TRUE, add = FALSE, main = "Estimates of scale parameter", ...)
s |
Point to evaluate the scale estimator in. |
Z |
Vector of |
kernel |
The kernel used in the estimator. One of |
h |
The bandwidth used in the kernel function. |
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 scale estimator is computed as
\hat{A}(s) = 1/(k+1) \sum_{i=1}^n 1_{Z_i>Z_{n-k,n}} K_h(s-i/n)
with K_h(x)=K(x/h)/h,
K
the kernel function and h
the bandwidth.
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 |
A |
Vector of the corresponding scale estimates. |
Tom Reynkens
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
ProbReg
, QuantReg
, scale
, Hill
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)
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