Model averaging for global Fréchet regression

Kurisu, Daisuke and Otsu, Taisuke ORCID: 0000-0002-2307-143X (2024) Model averaging for global Fréchet regression. Journal of Multivariate Analysis. ISSN 0047-259X (In Press)

Text (FrechetMA-JMVA-rev-v1 (002)) - Accepted Version Pending embargo until 1 January 2100. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (726kB)

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:	© 2024
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics
Date Deposited:	02 Jan 2025 10:42
Last Modified:	02 Jan 2025 11:03
URI:	http://eprints.lse.ac.uk/id/eprint/126533

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