We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data.
Jul 11, 2022 � We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional�...
Jul 18, 2024 � We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are�...
We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data,�...
Our key step to achieving the nonlinear sufficient dimension reduction is to build universal kernels on the metric spaces, which results in reproducing kernel�...
Jul 1, 2024 � Dimension reduction is helpful and often necessary in exploring nonlinear or nonparametric regression structures with a large number of�...
Nonlinear sufficient dimension reduction for distribution-on-distribution regression � List of references � Publications that cite this publication.
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In this paper we introduce a general theory for nonlinear sufficient di- mension reduction, and explore its ramifications and scope. This theory sub-.
Zhang, Q., Li, B. and Xue, L.* (2024) Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution Regression. (link, arXiv) Journal of�...
We propose a general theory and the estimation procedures for nonlinear sufficient dimension reduction where both the predictor and the response may.