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A New Hybrid Estimation Method for the Generalized Pareto Distribution
- Publication Year :
- 2015
- Publisher :
- Taylor & Francis, 2015.
-
Abstract
- The generalized Pareto distribution (GPD) is important in the analysis of extreme values, especially in modeling exceedances over thresholds. Most of the existing methods for estimating the scale and shape parameters of the GPD suffer from theoretical and/or computational problems. A new hybrid estimation method is proposed in this article, which minimizes a goodness-of-fit measure and incorporates some useful likelihood information. Compared with the maximum likelihood method and other leading methods, our new hybrid estimation method retains high efficiency, reduces the estimation bias, and is computation friendly.
- Subjects :
- Statistics and Probability
Mathematical optimization
Bayes estimator
021103 operations research
Estimation theory
0211 other engineering and technologies
02 engineering and technology
Trimmed estimator
Maximum likelihood sequence estimation
01 natural sciences
Measure (mathematics)
010104 statistics & probability
Generalized Pareto distribution
0101 mathematics
Computational problem
Extreme value theory
Mathematics
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....3aa0245e02739d3566d9e9712b5586e1
- Full Text :
- https://doi.org/10.6084/m9.figshare.1600883.v2