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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 |
| Reference manual: | smoothtail.pdf |
| Package source: | smoothtail_2.0.5.tar.gz |
| Windows binaries: | r-devel: smoothtail_2.0.5.zip, r-release: smoothtail_2.0.5.zip, r-oldrel: smoothtail_2.0.5.zip |
| macOS binaries: | r-release (arm64): smoothtail_2.0.5.tgz, r-oldrel (arm64): smoothtail_2.0.5.tgz, r-release (x86_64): smoothtail_2.0.5.tgz, r-oldrel (x86_64): smoothtail_2.0.5.tgz |
| Old sources: | smoothtail archive |
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