Design Of Experiments In Nonlinear Models: Asymptotic Normality, Optimality Criteria And Small-sample Properties (lecture Notes In Statistics)
by Andrej Pazman /
2013 / English / PDF
5.7 MB Download
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensivecoverage 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 theconnections 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 criteriabased on those asymptotic properties are then presented thoroughly in a special chapter.
Three chapters are dedicated to specificissues raised by nonlinear models. The construction of designcriteria 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.
