SMS scnews item created by John Ormerod at Mon 18 Sep 2017 2027
Type: Seminar
Distribution: World
Expiry: 22 Sep 2017
Calendar1: 22 Sep 2017 1400-1500
CalLoc1: Carslaw 173
CalTitle1: Clustering and classification of batch data
Auth: (jormerod) in SMS-WASM

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


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.

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