Date: 26 February 2020
Time: 12:45 - 13:30 (Lunch available from 12:35)
Room: RA 1501 (Ravelijn)
Speaker: Michael Vogt (University of Bonn)
Title: Clustering with statistical error control
Abstract:
he talk presents a clustering approach that allows for rigorous statistical error control similar to a statistical test. We develop estimators for both the unknown number of clusters and the clusters themselves. The estimators depend on a tuning parameter alpha which is similar to the significance level of a statistical hypothesis test. By choosing alpha, one can control the probability of overestimating the true number of clusters, while the probability of underestimation is asymptotically negligible. In addition, the probability that the estimated clusters differ from the true ones is controlled. In the theoretical part of the talk, formal versions of these statements on statistical error control are derived in a baseline model with convex clusters. The methodology is illustrated by an application to gene expression microarray data.