Broadcasted nonparametric tensor regression
Ya Zhou, Raymond K W Wong, Kejun He
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2024 · Volume 86 · Issue 5
Abstract
We propose a novel use of a broadcasting operation, which distributes univariate functions to all entries of the tensor covariate, to model the nonlinearity in tensor regression nonparametrically. A penalized estimation and the corresponding algorithm are proposed. Our theoretical investigation, which allows the dimensions of the tensor covariate to diverge, indicates that the proposed estimation yields a desirable convergence rate. We also provide a minimax lower bound, which characterizes the optimality of the proposed estimator for a wide range of scenarios. Numerical experiments are conducted to confirm the theoretical findings, and they show that the proposed model has advantages over its existing linear counterparts.
Keywords
elastic-net penalization, minimax lower bound, nonlinear regression, polynomial splines, tensor low rank
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