A hybrid estimator for generalized pareto and extreme-value distributions

The methods of moments and probability-weighted moments are the most commonly used methods for estimating the parameters of the generalized Pareto distribution and generalized extreme-value distributions. These methods, however, frequently lead to nonfeasible estimates in the sense that the supports...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Theory and methods 1998-01, Vol.27 (4), p.925-941
Main Authors: Dupuis, D.J., Tsao, M.
Format: Article
Language:English
Subjects:
auxiliary constraint
Distribution theory
Exact sciences and technology
feasible estimates
hybrid estimator
Mathematics
method of moments
method of probability-weighted mements
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Statistics
Stochastic processes
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The methods of moments and probability-weighted moments are the most commonly used methods for estimating the parameters of the generalized Pareto distribution and generalized extreme-value distributions. These methods, however, frequently lead to nonfeasible estimates in the sense that the supports inferred from the estimates fail to contain all observations. In this paper, we propose a hybrid estimator which is derived by incorporating a simple auxiliary constraint on feasibility into the estimates. The hybrid estimator is very easy to use, always feasible, and also has smaller bias and mean square error in many cases. Its advantages are further illustrated through the analyses of two real data sets.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610929808832136