Given estimates for the dependence function of a bivariate extreme value copula at specified points, this function fits a natural cubic smoothing spline, that is constrained to fulfill all the conditions of a dependence function, to the given estimates via quadratic programming.
SplineFitDepFct(x, y =NULL, alpha =0.01, integ.value)
Arguments
x, y: vectors giving the coordinates of the points to be approximated. Alternatively a single plotting structure can be specified: see xy.coords.
alpha: real, the smoothing parameter for the smoothing splines.
integ.value: real, non-negative value that should be less than two; see Details
Returns
A function, created by splinefun, that evaluates the natural cubic spline that was fitted to the data.
Details
integ.value should be between 0 and 2. If a value is specified, then an additional constraint is added to the quadratic program to ensure that the integeral (over 0 to 1) of the second derivative of the spline is larger or equal to integ.value. Choosing values close to 2 may lead to quadratic programms on which solve.QP reports inconsistent constraints.
Examples
## Data from Hall and Tajvidi (2004, ANZJS)EstDF2 <- NonparEstDepFct(MaxTemp, convex =FALSE)## Plot modified Pickands Function and area in which## dependence function must lieplot(EstDF2, ylim = c(0.5,1), xlab ="w", ylab ="A(w)", type="l", lty="longdash")polygon(c(0,0.5,1,0), c(1,0.5,1,1))## Fit spline to Pickands function and add to plotsplfit <- SplineFitDepFct(EstDF2)curve(splfit, n =301, add =TRUE, lty ="dashed")
References
Hall, P. and Tajvidi, N. (2000). Distribution and dependence-function estimation for bivariate extreme-value distributions. Bernoulli 6 (5), 835--844. Doi:10.2307/3318758.
Hall, P. and Tajvidi, N. (2004). Prediction regions for bivariate extreme events. Australian & New Zealand Journal of Statistics 46 (1), 99--112. Doi:10.1111/j.1467-842X.2004.00316.x.