Date: 2014-11-28

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Model-based clustering via finite mixture models is a popular clustering method for finding hidden structures in data. The model is often assumed to be a finite mixture of multivariate normal distributions; however, flexible extensions have been developed over recent years. This talk demonstrates some methods employed in unsupervised, semi-supervised, and supervised classification that include skew-normal and skew-t mixture models. Both real and simulated data sets are used to demonstrate the efficacy of these techniques.

Speaker

Irene Vrbik is a postdoctoral fellow in the Department of Mathematics and Statistics at McGill. She holds a PhD from the University of Guelph.