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Some explanations about the IWLS algorithm to fit generalized linear models

Author

Listed:
  • Christophe Dutang

    (LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université)

Abstract

This short note focuses on the estimation procedure, an iterative weighted least square method, generally used for generalized linear models.

Suggested Citation

  • Christophe Dutang, 2017. "Some explanations about the IWLS algorithm to fit generalized linear models," Working Papers hal-01577698, HAL.
    • Handle: RePEc:hal:wpaper:hal-01577698
    Note: View the original document on HAL open archive server: https://hal.science/hal-01577698
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    File URL: https://hal.science/hal-01577698/document
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    Cited by:

    1. Mithun S. Ullal & Iqbal Thonse Hawaldar & Rashmi Soni & Mohammed Nadeem, 2021. "The Role of Machine Learning in Digital Marketing," SAGE Open, , vol. 11(4), pages 21582440211, October.
    2. Capatina, Alexandru & Kachour, Maher & Lichy, Jessica & Micu, Adrian & Micu, Angela-Eliza & Codignola, Federica, 2020. "Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    3. Anne-Sophie Krah & Zoran Nikolić & Ralf Korn, 2020. "Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies," Risks, MDPI, vol. 8(1), pages 1-79, February.
    4. Anne-Sophie Krah & Zoran Nikoli'c & Ralf Korn, 2019. "Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies," Papers 1909.02182, arXiv.org.

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