Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties - Softcover
Synopsis
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensiveacoverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments.aThe first three chapters expose theaconnections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, amodels with heteroscedastic errors, etc. Classical optimality criteriaabased on those asymptotic properties are then presented thoroughly in a special chapter.aThree chapters are dedicated to specificaissues raised by nonlinear models. The construction of designacriteria derived from non-asymptotic considerations (small-sample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated.aA survey of algorithmic methods for the construction of optimal designs is provided."
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