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Activity Number: 471 - Advances in High-Dimensional Inference and Multiple Testing
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #305359
Title: Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
Author(s): Fei Wang* and Ling Zhou and Lu Tang and Peter X.K. Song
Companies: CarGurus and Southwestern University of Finance and Economics and University of Pittsburgh and School of Public Health, University of Michigan
Keywords: Confidence region; High dimension; LASSO; Ridge

Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters.In this paper, we consider high-dimensional linear regression modeling which a regularization procedure such as LASSO is applied to yield a sparse model. To establish a simultaneous post-model selection inference, we propose a method of contraction and expansion(MOCE) along the line of debiasing estimation that enables us to balance the bias-and-variance trade-off so that the super-sparsity assumption may be relaxed. We establish key theoretical results for the proposed MOCE procedure from which the expanded model can be selected with theoretical guarantees and simultaneous confidence regions can be constructed by the joint asymptotic normal distribution.In comparison with existing methods, our proposed method exhibits stable and reliable coverage at a nominal significance level with substantially less computational burden, and thus it is trustworthy for its application in solving real-world problems

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

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