Principles Of Neural Model Identification, Selection And Adequacy: With Applications To Financial Econometrics

28 mai 1999|
Principles Of Neural Model Identification, Selection And Adequacy: With Applications To Financial Econometrics de Achilleas Zapranis
181,95 $
Couverture souple
Obtenez 910 points privilègeᴹᴰ
Acheter en ligne
Livraison à une adresse
Expédition gratuite pour les commandes d’au moins 35 $
Cueillette en magasin
Pour savoir si la cueillette en magasin est offerte,
Acheter en magasin
Non vendu en magasin
Les prix et les offres peuvent différer de ceux en magasin

description

Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have been estimated. This is particularly important in the majority of financial applications where the data generating processes are dominantly stochastic and only partially deterministic. Based on the latest, most significant developments in estimation theory, model selection and the theory of mis-specified models, this volume develops neural networks into an advanced financial econometrics tool for non-parametric modelling. It provides the theoretical framework required, and displays the efficient use of neural networks for modelling complex financial phenomena. Unlike most other books in this area, this one treats neural networks as statistical devices for non-linear, non-parametric regression analysis.
Titre :Principles Of Neural Model Identification, Selection And Adequacy: With Applications To Financial Econometrics
Format :Couverture souple
Dimensions de l'article :200 pages, 9.25 X 6.1 X 0 po
Dimensions à l'expédition :200 pages, 9.25 X 6.1 X 0 po
Publié le :28 mai 1999
Publié par :Springer London
Langue :anglais
Convient aux âges :Tous les âges
ISBN - 13 :9781852331399

Recherche d’articles semblables, par catégorie :

Consulté récemment
|