Principles of Neural Model Identification, Selection and Adequacy : With Applications to Financial Econometrics

Apostolos-Paul N. Refenes

ISBN 10: 1852331399 / ISBN 13: 9781852331399

Published by Springer London, 1999

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781852331399

Bibliographic Details

• Title: Principles of Neural Model Identification, Selection and Adequacy : With Applications to Financial Econometrics
• Publisher: Springer London
• Publication Date: 1999
• Language: English
• ISBN 10: 1852331399
• ISBN 13: 9781852331399
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.