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Model averaging for global Fréchet regression

Author

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  • Kurisu, Daisuke
  • Otsu, Taisuke

Abstract

Non-Euclidean complex data analysis becomes increasingly popular in various fields of data science. In a seminal paper, Petersen and Müller (2019) generalized the notion of regression analysis to non-Euclidean response objects. Meanwhile, in the conventional regression analysis, model averaging has a long history and is widely applied in statistics literature. This paper studies the problem of optimal prediction for non-Euclidean objects by extending the method of model averaging. In particular, we generalize the notion of model averaging for global Fréchet regressions and establish an optimal property of the cross-validation to select the averaging weights in terms of the final prediction error. A simulation study illustrates excellent out-of-sample predictions of the proposed method.

Suggested Citation

  • Kurisu, Daisuke & Otsu, Taisuke, 2025. "Model averaging for global Fréchet regression," LSE Research Online Documents on Economics 126533, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:126533
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    File URL: http://eprints.lse.ac.uk/126533/
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    More about this item

    Keywords

    asymptotic optimality; cross validation; global Fréchet regression; model averaging;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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