NDLI: Likelihood-Based Inference for Extreme Value Models

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Likelihood-Based Inference for Extreme Value Models

Content Provider	Springer Nature Link
Author	Coles, Stuart G. Dixon, Mark J.
Copyright Year	1999
Abstract	Estimation of the extremal behavior of a process is often based on the fitting of asymptotic extreme value models to relatively short series of data. Maximum likelihood has emerged as a flexible and powerful modeling tool in such applications, but its performance with small samples has been shown to be poor relative to an alternative fitting procedure based on probability weighted moments. We argue here that the small-sample superiority of the probability weighted moments estimator is due to the assumption of a restricted parameter space, corresponding to finite population moments. To incorporate similar information in a likelihood-based analysis, we propose a penalized maximum likelihood estimator that retains the modeling flexibility and large-sample optimality of the maximum likelihood estimator, but improves on its small-sample properties. The properties of the penalized likelihood estimator are verified in a simulation study, and in application to sea-level data, which also enables the procedure to be evaluated in the context of structural models for extremes.
Starting Page	5
Ending Page	23
Page Count	19
File Format	PDF
ISSN	13861999
Journal	Extremes
Volume Number	2
Issue Number	1
e-ISSN	1572915X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1999-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hydrogeology Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Quality Control, Reliability, Safety and Risk Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Economics, Econometrics and Finance Engineering

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