Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74479
Title: Minimum distance estimation for the generalized pareto distribution
Authors: Chen, P
Ye, ZS
Zhao, X 
Keywords: Consistency
Extreme value
M-estimation
Peak over threshold
Regression
Issue Date: 2017
Publisher: American Statistical Association
Source: Technometrics, 2017, v. 59, no. 4, p. 528-541 How to cite?
Journal: Technometrics 
Abstract: The generalized Pareto distribution (GPD) is widely used for extreme values over a threshold. Most existing methods for parameter estimation either perform unsatisfactorily when the shape parameter k is larger than 0.5, or they suffer from heavy computation as the sample size increases. In view of the fact that k > 0.5 is occasionally seen in numerous applications, including two illustrative examples used in this study, we remedy the deficiencies of existing methods by proposing two new estimators for the GPD parameters. The new estimators are inspired by the minimum distance estimation and the M-estimation in the linear regression. Through comprehensive simulation, the estimators are shown to perform well for all values of k under small and moderate sample sizes. They are comparable to the existing methods for k < 0.5 while perform much better for k > 0.5.
URI: http://hdl.handle.net/10397/74479
ISSN: 0040-1706
DOI: 10.1080/00401706.2016.1270857
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