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- W1669809845 abstract "Publisher Summary This chapter discusses various approaches for the robust estimation of mixed models. It summarizes estimation based on maximising the Gaussian likelihood and discusses estimation based on maximising a Student t likelihood and other modifications to the Gaussian likelihood. The concept of restricted maximum likelihood estimation (REML), robust REML estimation, and Fellner's algorithmic approach are described in the chapter. The classical mixed linear model is obtained by assuming that the error components have Gaussian distributions. In this case, estimation is relatively straightforward. However, in practice, any of the observed error components can contain outliers, which result in non-Gaussian distributions for the error components and hence the response. Outlier contamination is often usefully represented by a mixture model in which a Gaussian kernel or core model is mixed with a contaminating distribution. Within this framework, various objectives can be entertained. Depending on the context, following objectives can be considered (1) estimating the parameters of the distributions of the error components, (2) estimating the variances of the distributions of the error components, whatever they happen to be, and (3) estimating the parameters of the core Gaussian distributions." @default.
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- W1669809845 date "1997-01-01" @default.
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- W1669809845 title "13 Approaches to the robust estimation of mixed models" @default.
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