Research on Bayesian Model Averaging for Lasso Based on Analysis of Scientific Materials

Abstract:

Article Preview

The Lasso (least absolute shrinkage and selection operator) estimates a vector of regression coefficients by minimizing the residual sum of squares subject to a constraint on the -norm of coefficient vector, which has been an attractive technique for regularization and variable selection. In this paper, we study the Bayesian Model Averaging(BMA) for Lasso, which accounts for the uncertainty about the best model to choose by averaging over multiple models. Experimental results on simulated data show that BMA has significant advantage over the model selection method based on Bayesian information criterion (BIC).

Info:

Periodical:

Advanced Materials Research (Volumes 282-283)

Edited by:

Helen Zhang and David Jin

Pages:

334-337

DOI:

10.4028/www.scientific.net/AMR.282-283.334

Citation:

A. T. Guo "Research on Bayesian Model Averaging for Lasso Based on Analysis of Scientific Materials", Advanced Materials Research, Vols. 282-283, pp. 334-337, 2011

Online since:

July 2011

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.