activity
20062008
most citedMixed membership analysis of high-throughput interaction studies: Relational data

10 citations · 33 across the 7 of their papers we have counts for

collaborators

7 papers

stat.AP20085 cited

Sequential category aggregation and partitioning approaches for multi-way contingency tables based on survey and census data

L. Fraser Jackson, Alistair G. Gray, Stephen E. Fienberg

Large contingency tables arise in many contexts but especially in the collection of survey and census data by government statistical agencies. Because the vast majority of the vari…

q-bio.QM20076 cited

Mixed membership analysis of genome-wide expression data

Edoardo M Airoldi, Stephen E Fienberg, Eric P Xing

Learning latent expression themes that best express complex patterns in a sample is a central problem in data mining and scientific research. For example, in computational biology…

stat.ME20074 cited

The William Kruskal Legacy: 1919--2005

Stephen E. Fienberg, Stephen M. Stigler, Judith M. Tanur

William Kruskal (Bill) was a distinguished statistician who spent virtually his entire professional career at the University of Chicago, and who had a lasting impact on the Institu…

stat.ME20071 cited

Maximum Likelihood Estimation in Latent Class Models For Contingency Table Data

S. E. Fienberg, P. Hersh, A. Rinaldo +1

Statistical models with latent structure have a history going back to the 1950s and have seen widespread use in the social sciences and, more recently, in computational biology and…

stat.AP20076 cited

A statistical approach to simultaneous mapping and localization for mobile robots

Anita Araneda, Stephen E. Fienberg, Alvaro Soto

Mobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latt…

q-bio.MN200710 cited

Mixed membership analysis of high-throughput interaction studies: Relational data

Edoardo M Airoldi, David M Blei, Stephen E Fienberg +1

In this paper, we consider the statistical analysis of a protein interaction network. We propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way p…