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DTSTART:20190310T070000
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DTSTART:20191103T060000
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DTSTART;TZID=America/Toronto:20200114T100000
SEQUENCE:0
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/department
 -seminar-lucy-gao-university-washington
LOCATION:M3 - Mathematics 3 200 University Avenue West Room 3127 Waterloo O
 N N2L 3G1 Canada
SUMMARY:Department seminar by Lucy Gao\, University of Washington
CLASS:PUBLIC
DESCRIPTION:STATISTICAL INFERENCE FOR MULTI-VIEW CLUSTERING\n\nIn the multi
 -view data setting\, multiple data sets are collected on a\nsingle\, commo
 n set of observations. For example\, we might perform\ngenomic and proteom
 ic assays on a single set of tumour samples\, or we\nmight collect relatio
 nship data from two online social networks for a\nsingle set of users. It 
 is tempting to cluster the observations using\nall of the data views\, in 
 order to fully exploit the available\ninformation. However\, clustering th
 e observations using all of the\ndata views implicitly assumes that a sing
 le underlying clustering of\nthe observations is shared across all data vi
 ews. If this assumption\ndoes not hold\, then clustering the observations 
 using all data views\nmay lead to spurious results. We seek to evaluate th
 e assumption that\nthere is some underlying relationship among the cluster
 ings from the\ndifferent data views\, by asking the question: are the clus
 ters within\neach data view dependent or independent? We develop new tests
  for\nanswering this question based on multivariate and/or network data\nv
 iews\, and apply them to multi-omics data from the Pioneer 100\nWellness S
 tudy (Price and others\, 2017) and protein-protein\ninteraction data from 
 the HINT database (Das and Yu\, 2012). We will\nalso briefly discuss our c
 urrent work on testing for no difference\nbetween the means of two estimat
 ed clusters in a single-view data set.\nThis is joint work with Jacob Bien
  (University of Southern California)\nand Daniela Witten (University of Wa
 shington).
DTSTAMP:20260411T011544Z
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