### Department of Biostatistics Seminar/Workshop Series

# Advances in Partition Modeling for Biomedical Research

## Matthew S. Shotwell (Matt)

### Graduate Student, Division of Biostatistics and Epidemiology, Department of Medicine, Medical University of South Carolina

### Wednesday, October 27, 2010, 1:30-2:30pm, MRBIII Conference Room 1220

### Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

Partition modeling is used to account for heterogeneity in biomedicalphenomena by partitioning similar observations into clusters. The Dirichlet process mixture is a partition model that encodes prior preference for unbalanced partitions, making it suitable, for example, in outlier detection problems. A new class of partition models is presented that parameterizes the prior preference for more or less balanced partitions. A novel estimation method and its implementation are discussed. The methods are assessed in the context of facial image recognition, where the partition of images is known, and in a yeast microarray time series, where the partition of probes is unknown.