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International Conference on Soft Methods in Probability and Statistics

SMPS 2016: Soft Methods for Data Science pp 495–502Cite as

A Nonparametric Linearity Test for a Multiple Regression Model with Fuzzy Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 456))

Abstract

A linearity test for a multiple regression model with LR-fuzzy responses and LR-fuzzy explanatory variables is considered. The regression model consists of several multiple regression models from response center or spreads to the explanatory centers and spreads. A multiple nonparametric regression model to be employed as a reference in the testing approach is estimated, and with which the linearity of the regression model is tested. Some simulation example is also presented.

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References

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Acknowledgments

Financial support from the national natural science foundation of China (NNSFC) with grant number 11271096, is kindly acknowledged.

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Authors and Affiliations

  1. Department of Statistics, Guangzhou University, No. 230 Waihuanxi Road, Higher Education Mega Center, Guangzhou, 510006, China

    Dabuxilatu Wang

Authors
  1. Dabuxilatu Wang
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Correspondence to Dabuxilatu Wang .

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Editors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maria Brigida Ferraro

  2. Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Paolo Giordani

  3. Dept of Basic & Applied Sciences Engg, Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

  4. Dept of Stoc Metds,Polish Academy of Sci, Systems Research Institute, Warsaw, Poland

    Marek Gagolewski

  5. Dept of Statis&OR and MD, Univ de Oviedo, Oviedo, Spain

    María Ángeles Gil

  6. Dept of Stoc Metds,Polish Aca of Science, Systems Res Inst, Warsaw, Poland

    Przemysław Grzegorzewski

  7. Sys Res Inti, Dept of Stoch Methods, Polish Acadmy Sci in Warsaw, Warsaw, Poland

    Olgierd Hryniewicz

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Wang, D. (2017). A Nonparametric Linearity Test for a Multiple Regression Model with Fuzzy Data. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_61

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

  • eBook Packages: EngineeringEngineering (R0)

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