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Robust weighted likelihood estimators with an application to bivariate extreme value problems
Author(s) -
Dupuis Debbie J.,
Morgenthaler Stephan
Publication year - 2002
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315863
Subject(s) - bivariate analysis , estimator , extreme value theory , mathematics , parametric model , maximum likelihood , parametric statistics , univariate , statistics , econometrics , m estimator , computer science , multivariate statistics
The authors achieve robust estimation of parametric models through the use of weighted maximum likelihood techniques. A new estimator is proposed and its good properties illustrated through examples. Ease of implementation is an attractive property of the new estimator. The new estimator downweights with respect to the model and can be used for complicated likelihoods such as those involved in bivariate extreme value problems. New weight functions, tailored for these problems, are constructed. The increased insight provided by our robust fits to these bivariate extreme value models is exhibited through the analysis of sea levels at two East Coast sites in the United Kingdom.