Principles of Neural Model Identification, Selection and Adequacy - With Applications to Financial Econometrics (Paperback, Softcover reprint of the original 1st ed. 1999)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Perspectives in Neural Computing
Release date:	2001
First published:	1999
Authors:	Achilleas Zapranis • Apostolos-Paul N. Refenes
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	190
Edition:	Softcover reprint of the original 1st ed. 1999
ISBN-13:	978-1-85233-139-9
Categories:	Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Business & Economics > Economics > Econometrics > General Books > Business & Economics > Finance & accounting > Finance > General Books > Computing & IT > Computer software packages > Computer graphics software > General Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks Books > Money & Finance > General
LSN:	1-85233-139-9
Barcode:	9781852331399

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