Model averaging for global Fréchet regression

Kurisu, Daisuke and Otsu, Taisuke ORCID: 0000-0002-2307-143X (2025) Model averaging for global Fréchet regression. Journal of Multivariate Analysis, 207. ISSN 0047-259X

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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.

Item Type:	Article
Additional Information:	© 2025 The Authors
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics
Date Deposited:	02 Jan 2025 10:42
Last Modified:	17 Oct 2025 06:06
URI:	http://eprints.lse.ac.uk/id/eprint/126533

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