Given an object of class mev_gpd,
returns a matrix of parameter values to mimic
the estimation uncertainty.
Usage
gpd.boot(object, B = 1000L, method = c("post", "norm"))Arguments
- object
object of class
mev_gpd- B
number of pairs to sample
- method
string; one of
'norm'for the normal approximation or'post'(default) for posterior sampling
Details
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.