BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20260625T071248EDT-7984eOtsJk@132.216.98.100 DTSTAMP:20260625T111248Z DESCRIPTION:Title: Statistical Inference for Multi-View Clustering\n\nAbstr act: In the multi-view data setting\, multiple data sets are collected on a single\, common set of observations. For example\, we might perform geno mic and proteomic assays on a single set of tumour samples\, or we might c ollect relationship data from two online social networks for a single set of users. It is tempting to cluster the observations using all of the data views\, in order to fully exploit the available information. However\, cl ustering the observations using all of the data views implicitly assumes t hat a single underlying clustering of the observations is shared across al l data views. If this assumption does not hold\, then clustering the obser vations using all data views may lead to spurious results. We seek to eval uate the assumption that there is some underlying relationship among the c lusterings from the different data views\, by asking the question: are the clusters within each data view dependent or independent? We develop new t ests for answering this question based on multivariate and/or network data views\, and apply them to multi-omics data from the Pioneer 100 Wellness Study (Price and others\, 2017) and protein-protein interaction data from the HINT database (Das and Yu\, 2012). We will also briefly discuss our cu rrent work on testing for no difference between the means of two estimated clusters in a single-view data set. This is joint work with Jacob Bien (U niversity of Southern California) and Daniela Witten (University of Washin gton).\n\n \n DTSTART:20191209T203000Z DTEND:20191209T213000Z LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue Sherbrooke Ouest SUMMARY:Lucy Gao (University of Washington) URL:/mathstat/channels/event/lucy-gao-university-washi ngton-303097 END:VEVENT END:VCALENDAR