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Sliced inverse regression method for multivariate compositional data modeling

Author

Listed:
  • Huiwen Wang

    (Beihang University
    Beijing Advanced Innovation Center for Big Data and Brain Computing)

  • Zhichao Wang

    (Beihang University
    Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations)

  • Shanshan Wang

    (Beihang University
    Beijing Advanced Innovation Center for Big Data and Brain Computing)

Abstract

Compositional data modeling is of great practical importance, as exemplified by applications in economic and geochemical data analysis. In this study, we investigate the sliced inverse regression (SIR) procedure for multivariate compositional data with a scalar response. We can achieve dimension reduction for the original multivariate compositional data quickly and then conduct a regression on the dimensional-reduced compositions. It is documented that the proposed method is successful in detecting effective dimension reduction directions, which generalizes the theoretical framework of SIR to multivariate compositional data. Comprehensive simulation studies are conducted to evaluate the performance of the proposed SIR procedure and the simulation results show its feasibility and effectiveness. A real data application is finally used to illustrate the success of the proposed SIR-based method.

Suggested Citation

  • Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:1:d:10.1007_s00362-019-01093-z
    DOI: 10.1007/s00362-019-01093-z
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    References listed on IDEAS

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