Some considerations on optimal design for non-linear mixed models
Seminar Room 1, Newton Institute
AbstractIn data analysis for life sciences mixed models, which involve both fixed and random effects, play an important role. Moreover, in this context many functional relationships are non-linear. These two features result in a parameter dependence of the (asymptotic) information matrix, which, as the inverse of the asymptotic covariance matrix for the maximum likelihood estimator, is meant to measure the performance of the underlying design of the study or experiment at hand. Consequently, the design optimization may and, in most cases, will be influenced by some of the model parameters. Recently, there has been some dispute on the most adequate form of the asymptotic information matrix obtained by linearization. In the present talk we will try to resolve this controversy and discuss the adequacy of some of the most popular design criteria. The ideas will be illustrated by some basic examples.
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