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Neural networks and logistic regression: Part I

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  • Schumacher, Martin
  • Ro[ss]ner, Reinhard
  • Vach, Werner

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  • Schumacher, Martin & Ro[ss]ner, Reinhard & Vach, Werner, 1996. "Neural networks and logistic regression: Part I," Computational Statistics & Data Analysis, Elsevier, vol. 21(6), pages 661-682, June.
    • Handle: RePEc:eee:csdana:v:21:y:1996:i:6:p:661-682
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    References listed on IDEAS

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    1. Vach, Werner & Ro[ss]ner, Reinhard & Schumacher, Martin, 1996. "Neural networks and logistic regression: Part II," Computational Statistics & Data Analysis, Elsevier, vol. 21(6), pages 683-701, June.
    2. J. Engel, 1988. "Polytomous logistic regression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 42(4), pages 233-252, December.
    3. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
    4. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
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    1. Marie Lebreton & Katia Melnik, 2009. "Voluntary Participation as a Determinant of Social Capital in France : Allowing for Parameter Heterogeneity," Working Papers halshs-00410530, HAL.
    2. Gaudart, Jean & Giusiano, Bernard & Huiart, Laetitia, 2004. "Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 547-570, January.
    3. Rabiu Muazu Musa & Anwar P. P. Abdul Majeed & Zahari Taha & Siow Wee Chang & Ahmad Fakhri Ab. Nasir & Mohamad Razali Abdullah, 2019. "A machine learning approach of predicting high potential archers by means of physical fitness indicators," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-12, January.
    4. Reggiani, Aura & Nijkamp, Peter & Nobilio, Lucia, 1997. "Spatial modal patterns in European freight transport networks: results of neurocomputing and logit models," Serie Research Memoranda 0029, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    5. Leo Liberti, 2020. "Distance geometry and data science," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 271-339, July.
    6. Jiangping Gao & Xiangyang Shi & Linghui Li & Ziqiang Zhou & Junfeng Wang, 2022. "Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    7. Alex Nosenko & Yuan Cheng & Haiquan Chen, 2023. "Password and Passphrase Guessing with Recurrent Neural Networks," Information Systems Frontiers, Springer, vol. 25(2), pages 549-565, April.
    8. Peltonen, Tuomas A., 2006. "Are emerging market currency crises predictable? A test," Working Paper Series 571, European Central Bank.
    9. Manojit Chattopadhyay & Subrata Kumar Mitra, 2017. "Applicability and effectiveness of classifications models for achieving the twin objectives of growth and outreach of microfinance institutions," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 451-474, December.
    10. Zhang, G. Peter & Keil, Mark & Rai, Arun & Mann, Joan, 2003. "Predicting information technology project escalation: A neural network approach," European Journal of Operational Research, Elsevier, vol. 146(1), pages 115-129, April.
    11. H. Pourghasemi & H. Moradi & S. Fatemi Aghda, 2013. "Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 749-779, October.

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