The bayesian group-lasso for analyzing contingency tables

Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 26th Annual International Conference on Machine Learning, ICML'09
DOIs
StatePublished - 2009
Externally publishedYes
Event26th Annual International Conference on Machine Learning, ICML'09 - Montreal, QC, Canada
Duration: 14 Jun 200918 Jun 2009

Publication series

NameACM International Conference Proceeding Series
Volume382

Conference

Conference26th Annual International Conference on Machine Learning, ICML'09
Country/TerritoryCanada
CityMontreal, QC
Period14/06/0918/06/09

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