Paper Title:
Research on Bayesian Model Averaging for Lasso Based on Analysis of Scientific Materials
  Abstract

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)
Chapter
Chapter2: Material Science, Environment Science and Engineering
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
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Price
$32.00
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