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Uncertain linear regression model and its application

Author

Listed:
  • Haiying Guo

    (Hebei University of Engineering)

  • Xiaosheng Wang

    (Hebei University of Engineering)

  • Zhichao Gao

    (Hebei University of Engineering)

Abstract

Regression analysis is a statistical process for estimating the relationships among variables based on probability. Because not all the imprecise quantities can be described by random variables, it is necessary to investigate relationships between an uncertain variable and some other variables. In this paper, an uncertain linear regression model is established based on uncertainty theory. Then, the estimators of parameters are obtained in the proposed model by the empirical uncertainty distribution coming from experts’ experimental data. Finally, the uncertain linear regression model is applied to solve an estimate problem.

Suggested Citation

  • Haiying Guo & Xiaosheng Wang & Zhichao Gao, 2017. "Uncertain linear regression model and its application," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 559-564, March.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:3:d:10.1007_s10845-014-1022-4
    DOI: 10.1007/s10845-014-1022-4
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    References listed on IDEAS

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    1. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Adnan Aktepe & Emre Yanık & Süleyman Ersöz, 2021. "Demand forecasting application with regression and artificial intelligence methods in a construction machinery company," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1587-1604, August.

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