CVEK: Robust Estimation and Testing for Nonlinear Effects using Kernel Machine Ensemble

Wenying Deng; Jeremiah Zhe Liu; Erin Lake; Brent A. Coull

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arXiv · 1811.11025

CVEK: Robust Estimation and Testing for Nonlinear Effects using Kernel Machine Ensemble

Wenying Deng·Jeremiah Zhe Liu·Erin Lake·Brent A. Coull

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Abstract

The R package CVEK introduces a suite of flexible machine learning models and robust hypothesis tests for learning the joint nonlinear effects of multiple covariates in limited samples. It implements the Cross-validated Ensemble of Kernels (CVEK)(Liu and Coull 2017), an ensemble-based kernel machine learning method that adaptively learns the joint nonlinear effect of multiple covariates from data, and provides powerful hypothesis tests for both main effects of features and interactions among features. The R Package CVEK provides a flexible, easy-to-use implementation of CVEK, and offers a wide range of choices for the kernel family (for instance, polynomial, radial basis functions, Mat\'ern, neural network, and others), model selection criteria, ensembling method (averaging, exponential weighting, cross-validated stacking), and the type of hypothesis test (asymptotic or parametric bootstrap). Through extensive simulations we demonstrate the validity and robustness of this approach, and provide practical guidelines on how to design an estimation strategy for optimal performance in different data scenarios.

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Wenying Deng, Jeremiah Zhe Liu, Erin Lake, Brent A. Coull. 2018-11-26. CVEK: Robust Estimation and Testing for Nonlinear Effects using Kernel Machine Ensemble. https://arxiv.org/abs/1811.11025

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