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Activity Number: 353
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #316309 View Presentation
Title: Unified Approach to Variable Selection in Missing Data via Least Squares Approximation
Author(s): Eric Reyes* and Cody Roberts
Companies: and Rose-Hulman Institute of Technology
Keywords: Missing Data ; Variable Selection ; LASSO
Abstract:

Ad hoc methods continue to be widely employed for performing variable selection in the presence of missing data. Wang and Leng (JASA, 2007) presented a unified approach to LASSO estimation via a least squares approximation; we consider their approach in the context of missing data. Specifically, we apply their technique for variable selection in conjunction with multiple imputation and inverse weighting methods for addressing missing data. The result is a general procedure for performing variable selection while accounting for data which are subject to covariates missing at random. We present results from simulation studies which establish that our approach is substantially better than the complete-case analysis.


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