IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v10y1994i1p17-34.html
   My bibliography  Save this article

Comparative study of artificial neural network and statistical models for predicting student grade point averages

Author

Listed:
  • Gorr, Wilpen L.
  • Nagin, Daniel
  • Szczypula, Janusz

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:10:y:1994:i:1:p:17-34
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0169-2070(94)90046-9
    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. Dalrymple, Douglas J., 1987. "Sales forecasting practices: Results from a United States survey," International Journal of Forecasting, Elsevier, pages 379-391.
    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. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, pages 67-85.
    2. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    3. 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.
    4. 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.
    5. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, pages 3516-3531.
    6. 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.
    7. Richards, Timothy J. & Patterson, Paul M. & van Ispelen, Pieter, 1998. "Modeling Fresh Tomato Marketing Margins: Econometrics And Neural Networks," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 27(2), October.
    8. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    9. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    10. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2004. "Vector autoregressive models versus neural networks in forecasting: an application to Euro-inflation and divisia money," Money Macro and Finance (MMF) Research Group Conference 2003 5, Money Macro and Finance Research Group.
    11. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
    12. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
    13. 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.
    14. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.

    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:intfor:v:10:y:1994:i:1:p:17-34. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.