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Principles of Neural Model Identification, Selection and Adequacy: With Applications to Financial Econometrics (Perspectives in Neural Computing) - Softcover
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.
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- PublisherSpringer
- Publication date1999
- ISBN 10 1852331399
- ISBN 13 9781852331399
- BindingPaperback
- LanguageEnglish
- Number of pages190
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Principles of Neural Model Identification, Selection and Adequacy with Applications to Financial Econometrics (Perspectives in Neural Computing)
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Principles of Neural Model Identification, Selection and Adequacy : With Applications to Financial Econometrics
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Principles of Neural Model Identification, Selection and Adequacy
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Springer London Mai 1999, 1999
ISBN 10: 1852331399
ISBN 13: 9781852331399
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 204 pp. Englisch. Seller Inventory # 9781852331399
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Principles of Neural Model Identification, Selection and Adequacy: With Applications to Financial Econometrics (Perspectives in Neural Computing)
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Principles of Neural Model Identification, Selection and Adequacy: With Applications to Financial Econometrics (Perspectives in Neural Computing)
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Principles of Neural Model Identification, Selection and Adequacy
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ISBN 10: 1852331399
ISBN 13: 9781852331399
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comes with an Internet site containing data from the case study and demonstration softwareProvides the reader with a practical tool to address a specific problem in developing financial applicationsNeural networks have had considerable success in a . Seller Inventory # 4289379
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Principles of Neural Model Identification, Selection and Adequacy : With Applications to Financial Econometrics
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Taschenbuch. Condition: Neu. 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
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