IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v170y2006i2p567-577.html
   My bibliography  Save this article

Model selection in Neural Networks: Some difficulties

Author

Listed:
  • Curry, B.
  • Morgan, P.H.

Abstract

No abstract is available for this item.

Suggested Citation

  • Curry, B. & Morgan, P.H., 2006. "Model selection in Neural Networks: Some difficulties," European Journal of Operational Research, Elsevier, vol. 170(2), pages 567-577, April.
  • Handle: RePEc:eee:ejores:v:170:y:2006:i:2:p:567-577
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(04)00427-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
    2. Phillips, P.C.B., 1989. "Partially Identified Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(2), pages 181-240, August.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    4. Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Feng, 2010. "Neural network metamodeling for cycle time-throughput profiles in manufacturing," European Journal of Operational Research, Elsevier, vol. 205(1), pages 172-185, August.
    2. Curry, Bruce, 2007. "Neural networks and seasonality: Some technical considerations," European Journal of Operational Research, Elsevier, vol. 179(1), pages 267-274, May.
    3. Bruce Curry, 2007. "Parameter redundancy in neural networks: an application of Chebyshev polynomials," Computational Management Science, Springer, vol. 4(3), pages 227-242, July.
    4. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
    5. Fernández, Eduardo F. & Almonacid, Florencia & Garcia-Loureiro, Antonio J., 2015. "Multi-junction solar cells electrical characterization by neuronal networks under different irradiance, spectrum and cell temperature," Energy, Elsevier, vol. 90(P1), pages 846-856.
    6. Fernández, Eduardo F. & Almonacid, Florencia, 2014. "Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications," Energy, Elsevier, vol. 74(C), pages 941-949.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
    2. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
    3. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    4. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    5. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    6. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    7. Jun Kang & Hyun Jun Lee & Seung Hwan Jeong & Hee Soo Lee & Kyong Joo Oh, 2020. "Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    8. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    9. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    10. Iglesias Emma M., 2011. "Constrained k-class Estimators in the Presence of Weak Instruments," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-13, September.
    11. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
    12. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    13. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    14. Chi-Young Choi & Ling Hu & Masao Ogaki, 2005. "Structural Spurious Regressions and A Hausman-type Cointegration Test," RCER Working Papers 517, University of Rochester - Center for Economic Research (RCER).
    15. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    16. Werner Ploberger & Peter C.B. Phillips, 1998. "Rissanen's Theorem and Econometric Time Series," Cowles Foundation Discussion Papers 1197, Cowles Foundation for Research in Economics, Yale University.
    17. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    18. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    19. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    20. Donald W. K. Andrews & Patrik Guggenberger, 2015. "Identification- and Singularity-Robust Inference for Moment Condition," Cowles Foundation Discussion Papers 1978R2, Cowles Foundation for Research in Economics, Yale University, revised Jan 2019.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:170:y:2006:i:2:p:567-577. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.