Maximum Likelihood Estimation

Part of the Springer Texts in Statistics book series (STS)

Maximum likelihood is a very general method for estimation of model parameters. It has good properties in large samples and when a valid model is used. Therefore it has to be accompanied by a method that addresses model uncertainty. In this chapter, we give details of the method of maximum likelihood and compare two approaches to dealing with model uncertainty-selecting a model and combining estimators based on the alternative models.


Model Selection Mean Square Error Maximum Likelihood Estimation Bayesian Information Criterion Critical Region 
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© Springer 2008

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