edoc

The Bayesian group-Lasso for analyzing contingency tables

Sudhir Raman, and Thomas J. Fuchs, and Peter J. Wild, and Edgar Dahl, and Volker Roth, . (2009) The Bayesian group-Lasso for analyzing contingency tables. In: Proceedings of the 26 th International Conference on Machine Learning : Montréal, Canada, June 14 - 18, 2009. Madison, pp. 881-888.

Full text not available from this repository.

Official URL: http://edoc.unibas.ch/dok/A5253625

Downloads: Statistics Overview

Abstract

Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Shankar Raman, Sudhir
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Publisher:Omnipress
Note:Publication type according to Uni Basel Research Database: Conference paper
Related URLs:
Identification Number:
Last Modified:13 Sep 2013 07:58
Deposited On:22 Mar 2012 13:57

Repository Staff Only: item control page