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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 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.

Ketersediaan
#
Perpustakaan Pusat E547
E547
Tersedia
Informasi Detail
Judul Seri
Lecture Notes in Statistics
No. Panggil
E547
Penerbit
New York : Springer New York.,
Deskripsi Fisik
xv, 399hlm.
Bahasa
English
ISBN/ISSN
9781461463634
Klasifikasi
NONE
Tipe Isi
-
Tipe Media
computer
Tipe Pembawa
online resource
Edisi
1
Subjek
Statistik
Kuantitatif
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

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