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Department seminar by Dr. Yufeng Liu, University of North Carolina at Chapel HillExport this event to calendar

Monday, December 10, 2012 — 2:00 PM EST

Statistical significance of clustering for high dimensional data

Clustering methods provide a powerful tool for the exploratory analysis of high dimensional datasets, such as gene expression microarray data. A fundamental statistical issue in clustering is which clusters are “really there,” as opposed to being artifacts of the natural sampling variation. In this talk, I will present Statistical Significance of Clustering (SigClust) as a cluster evaluation tool. In particular, we define a cluster as data coming from a single Gaussian distribution and formulate the problem of assessing statistical significance of clustering as a testing procedure. Under this hypothesis testing framework, the cornerstone of our SigClust analysis is accurate estimation of those eigenvalues of the covariance matrix of the null multivariate Gaussian distribution. In this talk, we propose a likelihood based soft thresholding approach for the estimation of the covariance matrix eigenvalues. Our theoretical work and simulation studies show that our proposed SigClust procedure works remarkably well. Applications to some cancer microarray data examples demonstrate the usefulness of SigClust.
Location 
MC - Mathematics & Computer Building
3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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