Design of Experiments in Nonlinear Models Asymptotic Normality, Optimality Criteria and Small-Sample Properties /

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 wi...

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Détails bibliographiques
Cote:Libro Electrónico
Auteurs principaux: Pronzato, Luc (Auteur), Pázman, Andrej (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: New York, NY : Springer New York : Imprint: Springer, 2013.
Édition:1st ed. 2013.
Collection:Lecture Notes in Statistics, 212
Sujets:
Biometry.
Statistics .
Social sciences-Statistical methods.
Biostatistics.
Statistics.
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
Accès en ligne:Texto Completo
Table des matières:
  • Introduction
  • Asymptotic designs and uniform convergence. Asymptotic properties of the LS estimator
  • Asymptotic properties of M, ML and maximum a posteriori estimators
  • Local optimality criteria based on asymptotic normality
  • Criteria based on the small-sample precision of the LS estimator
  • Identifiability, estimability and extended optimality criteria
  • Nonlocal optimum design
  • Algorithms-a survey
  • Subdifferentials and subgradients
  • Computation of derivatives through sensitivity functions
  • Proofs
  • Symbols and notation
  • List of labeled assumptions
  • References.

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