Bayesian L1 Lasso for High Dimensional Data
Location
Atrium
Session Format
Poster Presentation
Research Area Topic:
Natural & Physical Sciences - Mathematics
Co-Presenters and Faculty Mentors or Advisors
Daniel Linder, Assistant Professor of Biostatistics, Georgia Southern University
Abstract
The necessity to perform variable selection and estimation in the high dimensional situation is increased with the introduction of new inventions and technologies, capable of generating enormous amounts of data on individual observations. In frequent settings, the sample size (n) is smaller than number of variables (p) which hinders the performance of traditional regression methods. It has been demonstrated that the shrinkage methods such as the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996; Hastie, Tibshirani & Friedman, 2009) outclass the traditional least squares estimates in the high dimensional situation; however, they suffer from convex optimization problem. In our study, we produce a Gibbs sampler, identical to the Gibbs sampler for the Bayesian Lasso (Park & Casella, 2008), but we introduce the absolute deviation loss function (L1 loss) describing as modified Bayesian lasso with L1 loss. It is demonstrated that the proposed method outperforms the LASSO and the Bayesian LASSO in terms of prediction accuracy and variable selection. Our method is also implemented on a real high dimensional data set.
Keywords
LASSO, Regression, High dimensional data, Loss function
Presentation Type and Release Option
Presentation (Open Access)
Start Date
4-24-2015 2:45 PM
End Date
4-24-2015 4:00 PM
Recommended Citation
Panchal, Viral, "Bayesian L1 Lasso for High Dimensional Data" (2015). GS4 Georgia Southern Student Scholars Symposium. 110.
https://digitalcommons.georgiasouthern.edu/research_symposium/2015/2015/110
Bayesian L1 Lasso for High Dimensional Data
Atrium
The necessity to perform variable selection and estimation in the high dimensional situation is increased with the introduction of new inventions and technologies, capable of generating enormous amounts of data on individual observations. In frequent settings, the sample size (n) is smaller than number of variables (p) which hinders the performance of traditional regression methods. It has been demonstrated that the shrinkage methods such as the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996; Hastie, Tibshirani & Friedman, 2009) outclass the traditional least squares estimates in the high dimensional situation; however, they suffer from convex optimization problem. In our study, we produce a Gibbs sampler, identical to the Gibbs sampler for the Bayesian Lasso (Park & Casella, 2008), but we introduce the absolute deviation loss function (L1 loss) describing as modified Bayesian lasso with L1 loss. It is demonstrated that the proposed method outperforms the LASSO and the Bayesian LASSO in terms of prediction accuracy and variable selection. Our method is also implemented on a real high dimensional data set.
