NDLI: Bayesian approach to parameter estimation of the generalized pareto distribution

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Resampling-based multiple testing for microarray data analysis

On Bayesian interval prediction of future records

A common conjugate prior structure for several randomized response models

Posterior distributions for functions of variance components

First-passage problems for degenerate two-dimensional diffusion processes

Power and sample size calculation for 2×2 tables under multinomial sampling with random loss

Predicting the number of accidents at a road junction

Joint sensitivity in bayesian decision theory

Testing for stationarity in series with a shift in the mean. A fredholm approach

Statistical inference for a finite optimal stopping problem with unknown transition probabilities

Differentiating random upper semicontinuous functions under the integral sign

Bayesian approach to parameter estimation of the generalized pareto distribution

Bayesian approach to parameter estimation of the generalized pareto distribution

Content Provider	Springer Nature Link
Author	Zea Bermudez, P. Turkman, M. A. Amaral
Copyright Year	2003
Abstract	Several methods have been used for estimating the parameters of the generalized Pareto distribution (GPD), namely maximum likelihood (ML), the method of moments (MOM) and the probability-weighted moments (PWM). It is known that for these estimators to exist, certain constraints have to be imposed on the range of the shape parameter,k, of the GPD. For instance, PWM and ML estimators only exist fork>−0.5 andk≤1, respectively. Moreover, and particularly for small sample sizes, the most efficient method to apply in any practical situation highly depends on a previous knowledge of the most likely values ofk. This clearly suggests the use of Bayesian techniques as a way of using prior information onk. In the present work, we address the issue of estimating the parameters of the GPD from a Bayesian point of view. The proposed approach is compared via a simulation study with ML, PWM and also with the elemental percentile method (EPM) which was developed by Castillo and Hadi (1997). The estimation procedure is then applied to two real data sets.
Starting Page	259
Ending Page	277
Page Count	19
File Format	PDF
ISSN	11330686
Journal	Test
Volume Number	12
Issue Number	1
e-ISSN	18638260
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Elemental percentile method Gibbs sampling generalized Pareto distribution maximum likelihood peaks over threshold probability-weighted moments Bayesian inference Statistics of extreme values; tail inference Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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Comparison of Parameter Estimation Methods for the Three-Parameter Generalized Pareto Distribution

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Bayesian approach to parameter estimation of the generalized pareto distribution

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