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Nonlinear Statistical Models: Mathematics and Its Applications, cartea 254

Autor Andrej Pázman
en Limba Engleză Paperback – 5 dec 2010
Nonlinear statistical modelling is an area of growing importance. This monograph presents mostly new results and methods concerning the nonlinear regression model.
Among the aspects which are considered are linear properties of nonlinear models, multivariate nonlinear regression, intrinsic and parameter effect curvature, algorithms for calculating the L2-estimator and both local and global approximation. In addition to this a chapter has been added on the large topic of nonlinear exponential families.
The volume will be of interest to both experts in the field of nonlinear statistical modelling and to those working in the identification of models and optimization, as well as to statisticians in general.
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Specificații

ISBN-13: 9789048142620
ISBN-10: 9048142628
Pagini: 272
Ilustrații: X, 260 p.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.39 kg
Ediția:Softcover reprint of hardcover 1st ed. 1993
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Mathematics and Its Applications

Locul publicării:Dordrecht, Netherlands

Public țintă

Research

Cuprins

1 Linear regression models.- 2 Linear methods in nonlinear regression models.- 3 Univariate regression models.- 4 The structure of a multivariate nonlinear regression model and properties of L2 estimators.- 5 Nonlinear regression models: computation of estimators and curvatures.- 6 Local approximations of probability densities and moments of estimators.- 7 Global approximations of densities of L2 estimators.- 8 Statistical consequences of global approximations especially in flat models.- 9 Nonlinear exponential families.- References.- Basic symbols.