Precise Tensor Product Smoothing via Spectral Splines

Nathaniel E. Helwig ()
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Nathaniel E. Helwig: Department of Psychology, University of Minnesota, 75 E River Road, Minneapolis, MN 55455, USA

Stats, 2024, vol. 7, issue 1, 1-20

Abstract: Tensor product smoothers are frequently used to include interaction effects in multiple nonparametric regression models. Current implementations of tensor product smoothers either require using approximate penalties, such as those typically used in generalized additive models, or costly parameterizations, such as those used in smoothing spline analysis of variance models. In this paper, I propose a computationally efficient and theoretically precise approach for tensor product smoothing. Specifically, I propose a spectral representation of a univariate smoothing spline basis, and I develop an efficient approach for building tensor product smooths from marginal spectral spline representations. The developed theory suggests that current tensor product smoothing methods could be improved by incorporating the proposed tensor product spectral smoothers. Simulation results demonstrate that the proposed approach can outperform popular tensor product smoothing implementations, which supports the theoretical results developed in the paper.

Keywords: generalized additive model; linear mixed model; multiple nonparametric regression; penalized spline; smoothing spline analysis of variance (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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