Workshop on Developments and Challenges in Mixture Models,
Bump Hunting and Measurement Error Models







June 2 to 4, 2002

Dively Center, Case Western Reserve University
Cleveland, Ohio

Bumps, components, clusters and atypical structures from real data often lead to scientific discoveries or reveal interesting phenomena of a population.  They are important in astronomy, biology, data mining, bioinformatics and in applications to virtually all natural and social sciences.  The wide interest in such structures has in the last decade led to significant developments in each of these areas: mixture models for component hunting; nonparametric methods for bump or mode hunting; methods for cluster and structure hunting; and Bayesian computational methods for model selection and latent variable mixture models.  Additionally, data often come with measurement errors or incomplete information.  These problems add additional challenges to component, bump and cluster hunting and lead to another area of active research.  Image sharpening can be also considered as an inferential problem involving measurement error models.

This special international workshop brings together scientists from different camps but all working towards a common theme: knowledge hunting from real data in scientific problems involving bumps, components, clusters and other related topics.  We'll exchange new ideas and discuss challenges.

Invited Sessions: Astronomy, Bayesian methods, Bioinformatics, Bump hunting, Classification and Clustering,  Image analysis, Measurement errors, Mixture models, Neyman Lectures: (a) Physical Sciences (b) Genetics

Organized by:
Jiayang Sun
Hemant Ishwaran

Sponsored by:
National Science Foundation
Institute of Mathematical Statistics
Case Western Reserve University (College of Arts and Sciences, Provost Office, Medical School)
Cleveland Clinic Foundation
American Statistical Association, Cleveland Chapter