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Activity Number: 129 - High-Dimensional Data and Inference
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304427
Title: A Generalized Framework for High-Dimensional Inference Using Leave-One-Covariate-Out LASSO Path
Author(s): Xiangyang Cao* and Karl Gregory and Dewei Wang
Companies: University of South Carolina and University of South Carolina and University of South Carolina
Keywords: High-dimensional inference; Variable importance ; Simultaneous inference; Bootstrap

We develop a generalized framework for high-dimensional inference by calculating the change in the LASSO solution path when one covariate is left out. The proposed procedure provides a novel way to calculate variable importance. For high-dimensional linear regression models, our procedure allows for the construction of p-values for testing whether each coefficient is equal to zero as well as for simultaneous testing of multiple hypotheses. Bootstrap techniques are used to construct the null distribution. For low-dimensional linear models, our method has essentially the same power as the t-test. Extensive simulations are provided to show the effectiveness of our method. In the high-dimensional setting, our proposed solution path based test achieves greater power than some other recently developed high-dimensional inference methods. Our method naturally extends to generalized linear models and can be applied to solution paths of other regularization methods.

Authors who are presenting talks have a * after their name.

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