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Flexible Non-parametric Regression Models for Compositional Response Data with Zeros

Author

Listed:
  • Michail Tsagris
  • Abdulaziz Alenazi
  • Connie Stewart

Abstract

Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical k-NN regression, termed a-k-NN regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the a-transformation.

Suggested Citation

  • Michail Tsagris & Abdulaziz Alenazi & Connie Stewart, 2023. "Flexible Non-parametric Regression Models for Compositional Response Data with Zeros," Working Papers 2306, University of Crete, Department of Economics.
    • Handle: RePEc:crt:wpaper:2306
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    File URL: https://economics.soc.uoc.gr/wpa/docs/2306.pdf
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    Cited by:

    1. Yang, Bowen & Wang, Dafang & Yu, Beike & Wang, Facheng & Chen, Shiqin & Sun, Xu & Dong, Haosong, 2024. "Research on online passive electrochemical impedance spectroscopy and its outlook in battery management," Applied Energy, Elsevier, vol. 363(C).

    More about this item

    Keywords

    compositional data; regression;  α-transformation; k-NN algorithm; kernel regression;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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