Model averaging for global Fréchet regression

Daisuke Kurisu and Taisuke Otsu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: asymptotic optimality; cross validation; global Fréchet regression; model averaging (search for similar items in EconPapers)
JEL-codes: C1 J1 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2025-05-31
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Published in Journal of Multivariate Analysis, 31, May, 2025, 207. ISSN: 0047-259X

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:126533

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