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gpdfit

Estimate parameters of the Generalized Pareto distribution

Description

Given a sample x, Estimate the parameters k and σ of the generalized Pareto distribution (GPD), assuming the location parameter is 0. By default the fit uses a prior for k, which will stabilize estimates for very small sample sizes (and low effective sample sizes in the case of MCMC samples). The weakly informative prior is a Gaussian prior centered at 0.5.

Usage

gpdfit(x, wip = TRUE, min_grid_pts = 30, sort_x = TRUE)

Arguments

`x`	A numeric vector. The sample from which to estimate the parameters.
`wip`	Logical indicating whether to adjust k based on a weakly informative Gaussian prior centered on 0.5. Defaults to `TRUE`.
`min_grid_pts`	The minimum number of grid points used in the fitting algorithm. The actual number used is `min_grid_pts + floor(sqrt(length(x)))`.
`sort_x`	If `TRUE` (the default), the first step in the fitting algorithm is to sort the elements of `x`. If `x` is already sorted in ascending order then `sort_x` can be set to `FALSE` to skip the initial sorting step.

Details

Here the parameter k is the negative of k in Zhang & Stephens (2009).

Value

A named list with components k and sigma.

References

Zhang, J., and Stephens, M. A. (2009). A new and efficient estimation method for the generalized Pareto distribution. Technometrics 51, 316-325.