angscdf | R Documentation |
This function computes smooth empirical-likelihood based estimators for the angular distribution function of a bivariate extreme value distribution.
angscdf(Y, tau = 0.95, nu, grid = seq(0.01, 0.99, length = 2^8), method = "euclidean", raw = TRUE)
Y |
data frame with two columns from which the estimate is to be computed. |
tau |
value used to threshold the data; by default it is set as
the 0.95 quantile of the pseudo-radius |
nu |
concentration parameter of beta distribution which controls the amount of smoothing. |
grid |
grid with coordinates of the points where the angular
measure is estimated; by default |
method |
a character string setting the method to be used. By
default |
raw |
logical; if |
method = "euclidean"
implements the maximum Euclidean
likelihood spectral distribution function as introduced by de
Carvalho et al (2013). method = "empirical"
implements the
maximum Empirical likelihood spectral distribution function as
introduced by Einmahl and Segers (2009).
H |
the estimated angular distribution function values. |
grid |
grid with coordinates of the points where the angular measure is estimated. |
w |
pseudo-angles. |
nu |
concentration parameter of the Beta-kernel. |
Y |
raw data. |
The plot
method depicts the empirical likelihood-based
angular distribution function.
Miguel de Carvalho
de Carvalho, M., Oumow, B., Segers, J. and Warchol, M. (2013) A Euclidean likelihood estimator for bivariate tail dependence. Communications in Statistics—Theory and Methods, 42, 1176–1192.
Einmahl, J. H. J., and Segers, J. (2009) Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution. The Annals of Statistics, 37, 2953–2989.
## de Carvalho et al (2013, Fig. 7) data(beatenberg) attach(beatenberg) fit <- angscdf(beatenberg, tau = 0.98, nu = 163, raw = FALSE) plot(fit) rug(fit$w)
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