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QA279.SPR FCT 81256 Plane answers to complex questions | QA279.SPR FCT 81337 Linear mixed-effects models using R | QA279.SPR FCT 81379 Smoothing spline ANOVA models | QA279.SPR FCT 81431 Design of experiments in nonlinear models | QA279.SPR FCT 81450 Advances in growth curve models | QA279.SPR FCT 81452 ISS-2012 proceedings volume on longitudinal data analysis subject to measurement errors, missing values, and/or outliers | QA279.SPR FCT 81821 mODa 10 |
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Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensive coverage 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. The first three chapters expose the connections 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, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter. Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria 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. A survey of algorithmic methods for the construction of optimal designs is provided.
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