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Uncertain regression analysis: an approach for imprecise observations

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Abstract

Regression analysis is a method to estimate the relationships among the response variable and the explanatory variables. Assuming the observations of the response variable are imprecise and modeling the observed data via uncertain variables, this paper explores an approach of uncertain regression analysis to estimating the relationships among the variables with imprecisely observed samples. On the principle of least squares, an optimization problem is derived to calculate the unknown parameters in the regression model. In particular, this paper investigates uncertain linear regression model and gives an analytic representation of the unknown parameters.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant Nos. 61403360 and 61573210) and the Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences.

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Authors and Affiliations

  1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Kai Yao

  2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China

    Kai Yao

  3. Department of Mathematical Sciences, Tsinghua University, Beijing, 100084, China

    Baoding Liu

Authors
  1. Kai Yao
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  2. Baoding Liu
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Correspondence to Kai Yao.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by Y. Ni.

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Yao, K., Liu, B. Uncertain regression analysis: an approach for imprecise observations. Soft Comput 22, 5579–5582 (2018). https://doi.org/10.1007/s00500-017-2521-y

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  • DOI: https://doi.org/10.1007/s00500-017-2521-y

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