Work

Projective-resampling minimum average variance estimation for multivariate regression

Year: 2025

Type: article

Abstract: This paper addresses the challenge of dimension reduction for a multivariate regression.We introduce a novel approach that integrates the minimum average variance estimation (Xia et al., 2002) with the projective resampling approach (Li et al., 2008) to render its applicability in multivariate response settings.By applying the minimum average variance estimation within the projective resampling framework, we achieve an effective dimension reduction, ensuring the exhaustive recovery of the central mean subspace and eradicating strong assumptions on the distribution of predictors.The proposed methodology is validated through intensive numerical studies to evaluate its effectiveness and robustness in various multivariate regressions.Further refinement made improves the computational performance as well.

Source: Communications for Statistical Applications and Methods

DOI: 10.29220/csam.2025.32.3.365

Authors: Seoyoung Kim, Jae Keun Yoo

Institution: Ewha Womans University

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, Ministry of Education, National Research Foundation of Korea

Awards: G3543619023, G4442277617, G4458541697, G4953959209