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Activity Number: 241
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305519
Title: Variable Selection with Prior Information for Generalized Linear Models via the pLasso Method
Author(s): Yuan Jiang*+ and Yunxiao He and Heping Zhang
Companies: Oregon State University and Yale University and Yale University
Address: 44 Kidder Hall, Corvallis, OR, 97331, United States
Keywords: Prior Information ; Variable Selection ; Genome-wide Association Studies ; Solution Path

Lasso is a popular variable selection tool and is often used in conjunction with generalized linear models. When the number of variables of interest is larger than the sample size, as in many biological/biomedical studies, the power of Lasso can be limited. However, intensive biological/biomedical researches have provided large amount of plausible information about the significance of certain variables. This paper proposes an extension of Lasso, named prior Lasso (pLasso), to incorporate that prior information into penalized generalized linear models. The goal is achieved by further penalizing the Lasso criterion function with a measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLasso estimator can be found with a procedure similar to Least Angle Regression. Asymptotic theory and simulation results show that pLasso provides significant improvement over Lasso when the prior information is relatively accurate. When the prior information is less reliable, pLasso shows great robustness from being distracted. We illustrate the application of pLasso using a real data set from genome-wide association studies.

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