Publication

Robust weighted likelihood estimators with an application to bivariate extreme value problems

Stephan Morgenthaler
2002
Journal Articles

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/11036614-9cd5-406a-a7f3-1de4754cd184

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Stephan Morgenthaler
2002
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/11036614-9cd5-406a-a7f3-1de4754cd184

About this result

Related concepts (28)

Robust statistics

Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to provide methods with good performance when there are small departures from a parametric distribution.

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

Parametric model

In statistics, a parametric model or parametric family or finite-dimensional model is a particular class of statistical models. Specifically, a parametric model is a family of probability distributions that has a finite number of parameters. A statistical model is a collection of probability distributions on some sample space. We assume that the collection, P, is indexed by some set Θ. The set Θ is called the parameter set or, more commonly, the parameter space.

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