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The Origins of Logistic Regression

Author

Listed:
  • J.S. Cramer

    (Faculty of Economics, University of Amsterdam)

Abstract
This paper describes the origins of the logistic function, its adoption in bio-assay, and its wider acceptance in statistics. Its roots spread far back to the early 19th century; the survival of the term logistic and the wide application of the device have been determined decisively by the personal histories and individual actions of a few scholars.

Suggested Citation

  • J.S. Cramer, 2002. "The Origins of Logistic Regression," Tinbergen Institute Discussion Papers 02-119/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20020119
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    File URL: https://papers.tinbergen.nl/02119.pdf
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    Cited by:

    1. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
    2. Beata Bieszk-Stolorz & Iwona Markowicz, 2021. "Risk of Decline in Share Prices of Energy and Fuel Sector on the Warsaw Stock Exchange During the Two Waves of the COVID-19 Pandemic," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 977-996.
    3. Jing Zhao & Hui Hu & Jinglei Wang, 2022. "Forest Carbon Reserve Calculation and Comprehensive Economic Value Evaluation: A Forest Management Model Based on Both Biomass Expansion Factor Method and Total Forest Value," IJERPH, MDPI, vol. 19(23), pages 1-15, November.
    4. Vladimir Vishnevsky & Valentina Klimenok & Alexander Sokolov & Andrey Larionov, 2021. "Performance Evaluation of the Priority Multi-Server System MMAP/PH/M/N Using Machine Learning Methods," Mathematics, MDPI, vol. 9(24), pages 1-27, December.
    5. Panagiotis Panagiotidis & Kyriakos Giannakis & Nikolaos Angelopoulos & Angelos Liapis, 2021. "Shipping Accidents Dataset: Data-Driven Directions for Assessing Accident’s Impact and Improving Safety Onboard," Data, MDPI, vol. 6(12), pages 1-19, December.
    6. Abdulrezzak Zekiye & Semih Utku & Fadi Amroush & Oznur Ozkasap, 2023. "AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors," Papers 2308.08554, arXiv.org.
    7. Yaozu Qin & Li Cao & Wenjing Li & Ali Darvishi Boloorani & Yuan Li & Xinxin Ke & Masoud Soleimani & Qian Yu & Cuimin Zhou, 2023. "Suitability Assessment Method of Red Tourism Development Using Geospatial and Social Humanity Data: A Case Study of Ruijin City, East China," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    8. Terence Mills & Kerry Patterson, 2013. "Carmichael's Arctan Trend: Precursor of Smooth Transition Functions," Economics Discussion Papers em-dp2013-06, Department of Economics, University of Reading.
    9. Christopher Bockel-Rickermann & Sam Verboven & Tim Verdonck & Wouter Verbeke, 2023. "A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions," Papers 2309.03730, arXiv.org.
    10. Minsu Chae & Hyo-Wook Gil & Nam-Jun Cho & Hwamin Lee, 2022. "Machine Learning-Based Cardiac Arrest Prediction for Early Warning System," Mathematics, MDPI, vol. 10(12), pages 1-17, June.
    11. Soumadip Ghosh & Arnab Hazra & Abhishek Raj, 2020. "A Comparative Study of Different Classification Techniques for Sentiment Analysis," International Journal of Synthetic Emotions (IJSE), IGI Global, vol. 11(1), pages 49-57, January.
    12. Bieszk-Stolorz Beata & Markowicz Iwona, 2014. "Economical Activity Of The Polish Population," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 198-210, December.
    13. Kerstin Lopatta & Mario Albert Gloger & Reemda Jaeschke, 2017. "Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings," Accounting Perspectives, John Wiley & Sons, vol. 16(4), pages 315-343, December.
    14. Bieszk-Stolorz Beata, 2012. "Economic Activity of the Disabled in Poland in 2010," Folia Oeconomica Stetinensia, Sciendo, vol. 10(2), pages 128-137, January.
    15. Miguel Cuartas & Estela Ruiz & Diego Ferreño & Jesús Setién & Valentín Arroyo & Federico Gutiérrez-Solana, 2021. "Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1739-1751, August.

    More about this item

    Keywords

    Logistic Regression; History;

    JEL classification:

    • B1 - Schools of Economic Thought and Methodology - - History of Economic Thought through 1925
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

    NEP fields

    This paper has been announced in the following NEP Reports:

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