Statistics Seminar: Sharon Lee (UQ) -- Clustering and classification of batch data

Abstract: 

Motivated by the analysis of batch cytometric data, we consider the problem 
of jointly modelling and clustering multiple heterogeneous data samples. 
Traditional mixture models cannot be applied directly to these data. 
Intuitive approaches such as pooling and post-hoc cluster matching fails to 
account for the variations between the samples. In this talk, we consider a 
hierarchical mixture model approach to handle inter-sample variations. The 
adoption of a skew mixture model with random effects terms for the location 
parameter allows for the simultaneous clustering and matching of clusters 
across the samples. In the case where data from multiple classes of objects 
are available, this approach can be further extended to perform classification 
of new samples into one of the predefined classes. Examples with real 
cytometry data will be given to illustrate this approach.