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Advances in Classification and Data Analysis pp 283–290Cite as

Large-sample Properties of Neural Estimators in a Regression Model with ϕ-mixing Errors1

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Abstract

In this paper the large sample properties of neural networks estimators in a regression model with ϕ-mixing errors are investigated. In particular, using the theory of M-estimators, it is proved that the minimum squared error estimators of the connection weights and of the fitted values are consistent and asymptotically Normal.

The paper is partially supported by MURST ‘98 “Modelli statistici per l’analisi delle serie temporali”

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References

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Economiche, Sichelgaita S.P.A. Società di Studi Economici e Sociali, Università degli Studi di Salerno, Italy

    Francesco Giordano

  2. Dipartimento di Scienze Economiche, Centro di Specializzazione e Ricerche, Università degli Studi di Salerno, Portici (Na), Italy

    Cira Perna

Authors
  1. Francesco Giordano
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  2. Cira Perna
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Editor information

Editors and Affiliations

  1. Department SEFEMEQ, University of Rome “Tor Vergata”, Via di Tor Vergata, 00133, Rome, Italy

    Simone Borra

  2. Department SEGeS, University of Molise, Via de Sanctis, 86100, Campobasso, Italy

    Roberto Rocci

  3. Department Statistica, Probabilità e Statistiche Applicate, University of Rome “La Sapienza”, Piazza le Aldo Moro, 00185, Rome, Italy

    Maurizio Vichi

  4. University of Mannheim, 68131, Mannheim, Germany

    Martin Schader (Lehrstuhl für Wirtschaftsinformatik III Schloß) (Lehrstuhl für Wirtschaftsinformatik III Schloß)

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© 2001 Springer-Verlag Berlin Heidelberg

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Giordano, F., Perna, C. (2001). Large-sample Properties of Neural Estimators in a Regression Model with ϕ-mixing Errors1 . In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_35

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  • DOI: https://doi.org/10.1007/978-3-642-59471-7_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41488-9

  • Online ISBN: 978-3-642-59471-7

  • eBook Packages: Springer Book Archive

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