Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior

Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes and discontinuous jumps on the boundaries. Moreover, the relationship between the response and the tensor covariates can be nonlinear. In this article, we develop a nonlinear Bayesian tensor additive regression model to accommodate such spatial structure. A functional fused elastic net prior is proposed over the additive component functions to comprehensively model the nonlinearity and spatial smoothness, detect the discontinuous jumps, and simultaneously identify the active regions. The great flexibility and interpretability of the proposed method against the alternatives are demonstrated by a simulation study and an analysis on facial feature data.

Automated summary

This article proposes a nonlinear Bayesian tensor additive regression model to predict tensor covariates with unknown shapes and discontinuous jumps. The proposed method uses a functional fused elastic net prior to model nonlinearity and spatial smoothness, detect discontinuous jumps, and identify active regions.