Given an object of class mev_gpd,
returns a matrix of parameter values to mimic
the estimation uncertainty.
gpd.boot(object, B = 1000L, method = c("post", "norm"))a matrix of size B by 2 whose columns contain scale and shape parameters
object of class mev_gpd
number of pairs to sample
string; one of 'norm' for the
normal approximation or 'post' (default) for posterior sampling
Two options are available: a normal approximation to the scale and shape based on the maximum likelihood estimates and the observed information matrix. This method uses forward sampling to simulate from a bivariate normal distribution that satisfies the support and positivity constraints
The second approximation uses the ratio-of-uniforms method to obtain samples from the posterior distribution with uninformative priors, thus mimicking the joint distribution of maximum likelihood. The benefit of the latter is that it is more reliable in small samples and when the shape is negative.