Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Principles of Neural Model Identification, Selection and Adequacy: With Applications to Financial Econometrics

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

Zapranis, Achilleas
Refenes, Apostolos-Paul N.

Paperback

Series: Perspectives in Neural Computing

General ComputersGeneral GardeningNetworking

ISBN10: 1852331399
ISBN13: 9781852331399
Publisher: Springer Nature
Published: May 28 1999
Pages: 190
Weight: 0.68
Height: 0.49 Width: 6.10 Depth: 9.16
Language: English
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.

Also in

General Gardening

View All