gpd.boot {mev}

Bootstrap approximation for generalized Pareto parameters

Description

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.

Value

a matrix of size B by 2 whose columns contain scale and shape parameters