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New method for dimension reduction with multimodal predictors introduced

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new method for sufficient dimension reduction in regression problems involving both a standard Euclidean predictor and a predictor object from a general metric space. The approach embeds the predictor object into a reproducing kernel Hilbert space and uses a regularized kernel-response envelope model to extract a finite-dimensional reduction. The method establishes root-n consistency rates for the envelope subspace and large-sample consistency for the Euclidean subspace estimator, with simulations and real-data applications demonstrating its effectiveness. AI

RANK_REASON The cluster contains a single academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]

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New method for dimension reduction with multimodal predictors introduced

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The cluster contains a single academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Abdul-Nasah Soale ·

    Sufficient Dimension Reduction with Multimodal Predictor Objects

    arXiv:2611.13460v1 Announce Type: cross Abstract: We study the regression of a scalar response $Y$ on a Euclidean predictor $\mathbf{X} \in \mathbb{R}^p$ and a random predictor object $\mathcal{Z}$ in a general metric space under arbitrary structural dependence. We introduce a jo…

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