gpdfit: Estimate parameters of the Generalized Pareto distribution

Description Usage Arguments Details Value References See Also

View source: R/gpdfit.R


Estimate the parameters k and σ of the generalized Pareto distribution (assuming location parameter is 0), given a sample x. By default the Pareto 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.


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



A numeric vector. The sample from which to estimate the parameters.


Logical indicating whether to adjust k based on a weakly informative Gaussian prior centered on 0.5. Defaults to TRUE.


The minimum number of grid points used in the fitting algorithm. The actual number used is min_grid_pts + floor(sqrt(length(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.


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


A named list with components k and sigma.


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

See Also

psis, pareto-k-diagnostic, loo-package

loo documentation built on April 11, 2018, 5:04 p.m.