A Scaling Neural Network-Based Approach for Variable Selection in Nonparametric Regression

Music City Center 

Variable selection is crucial in statistical modeling, especially in high-dimensional contexts where it improves interpretability and accuracy. We propose a neural network-based approach for variable selection in nonparametric regression models, incorporating L1 penalties and custom loss functions to encourage sparsity while maintaining deep learning flexibility. Our hybrid framework uses neural networks for feature selection, efficiently managing many variables. Comparisons with Bayesian Kernel Machine Regression (BKMR) show our method handles more variables, overcoming BKMR's computational limits. Simulation studies demonstrate our method effectively selects important variables and mitigates overfitting. This approach offers a scalable solution for high-dimensional modeling, with advantages over traditional methods in complex data structures, such as in environmental science research.

Variable Selection

Neural Network

Nonparametric

Environmental Science 

Section on Statistical Learning and Data Science