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...
שמור ב:
| Auteurs principaux: | , |
|---|---|
| פורמט: | Livre numérique |
| שפה: | Anglais |
| יצא לאור: |
New York, NY :
Springer New York
2013.
Cham : Springer Nature |
| סדרה: | Lecture Notes in Statistics
212 |
| גישה מקוונת: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| הערה: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Design of Experiments in Nonlinear Models, Texte imprimé, 9781461463627 • Design of Experiments in Nonlinear Models, Texte imprimé, 9781461463641 |
תוכן הענינים:
- 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.