Workshop B: Multiple Comparisons and Mixture Models for Large Data Sets

23 June 2003

All day

Symposium Home

Advances in information technology are now generating high-dimensional complex data. Important statistical methods that are being used to analyze the data include multiple comparisons and mixture models. Within the Bayesian paradigm, it is natural to approach multiple comparisons via mixture models, but many challenging conceptual and computational problems exist. False Discovery Rate (FDR) methods are also popular in analyzing large data via multiple comparisons. The challenge is to find a suitable place for FDR within the fully Bayesian approach to multiple comparisons. This workshop organized by Jim Berger and Jayanta Ghosh will bring current research perspectives on these issues for large data sets.

Partial List of Participants:

  • Sanjib Basu (North Illinois University)
  • Yoav Benjamini (Tel Aviv University, Israel)
  • Siddhartha Chib (Washington University)
  • Dipak Dey (University of Connecticut)
  • Helmut Finner (University of Dusseldorf, Germany)
  • Yosef Hochberg (Tel Aviv University, Israel)
  • Jiashun Jin (Stanford University)
  • Jiayang Sun (Case Western Reserve University)
  • Sanat Sarkar (Temple University)
Last Updated: Aug 31, 2017 4:46 PM

Purdue Department of Statistics, 250 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

© 2015 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue? Please contact the College of Science Webmaster.