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Activity Number: 314
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319807 View Presentation
Title: Stagewise Generalized Estimating Equations
Author(s): Gregory Vaughan* and Robert Aseltine and Kun Chen and Jun Yan
Companies: University of Connecticut and University of Connecticut Health Center and University of Connecticut and University of Connecticut
Keywords: bi-level selection ; boosting ; generalized estimating equations ; group selection ; sparsity ; stagewise estimation

Feature selection is routinely required in many contemporary statistical modeling tasks. To tackle the problem, there has been a revival of interest in the forward stagewise estimation methodology, where the main idea is to build up a model by conducting a sequence of simple learning steps to gradually increase the model complexity. Under the framework of generalized estimation equations (GEE), we study stagewise estimation approaches that handle clustered data using a variable grouping structure. In practice however, important groups may contain irrelevant variables; the key is thus to select at a group and individual level. We first propose a bi-level stagewise estimating equations (BiSEE) approach, and establish its correspondence to the sparse group lasso. We also propose a hierarchical stagewise estimating equations (HiSEE) approach, in which each estimation step is executed as a hierarchical selection process. Simulation studies show improved model selection and predictive performance of BiSEE and HiSEE compared to existing approaches. A study with Connecticut teen hospitalization data further showcases the efficacy of these approaches.

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

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