smoothtail: Smooth Estimation of GPD Shape Parameter
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log–concave density function. This procedure is justified by the fact that the GPD density is log–concave for gamma in [-1,0].
| Version: |
2.0.5 |
| Depends: |
logcondens (≥ 2.0.0) |
| Imports: |
stats |
| Published: |
2016-07-13 |
| Author: |
Kaspar Ru{f}{i}bach and Samuel Mueller |
| Maintainer: |
Kaspar Rufibach <kaspar.rufibach at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://www.kasparrufibach.ch,
www.maths.usyd.edu.au/ut/people?who=S_Mueller |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
smoothtail results |
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