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DTSTAMP:20250918T213756Z
DESCRIPTION:Object Oriented Data Analysis with Application to Neuroimaging 
 Studies\n\n\n	Abstract:\n\n\nIn this talk\, I will first briefly introduce 
 my research on object oriented data analysis with application to neuroimag
 ing studies. I will then talk about a detailed example on imaging genetics
 . In this project\, we develop a high-dimensional matrix linear regression
  model to correlate 2D imaging responses with high-dimensional genetic cov
 ariates. We propose a fast and efficient screening procedure based on the 
 spectral norm to deal with the case that the dimension of scalar covariate
 s is much larger than the sample size. We develop an efficient estimation 
 procedure based on the nuclear norm regularization\, which explicitly borr
 ows the matrix structure of coefficient matrices. We examine the finite-sa
 mple performance of our methods using simulations and a large-scale imagin
 g genetic dataset from the Alzheimer’s Disease Neuroimaging Initiative stu
 dy.\n\n\n	Speaker\n\n\nDehan Kong is an Assistant Professor in the Departme
 nt of Statistical Sciences at the University of Toronto. His research inte
 rests include Neuroimaging\, Statistical Machine Learning\, Functional Dat
 a and High Dimensional Data.\n\nOrganized by the9IÖÆ×÷³§Ãâ·Ñ Statistics Group\n
 \nSeminar website:https://mcgillstat.github.io/\n
DTSTART:20181026T193000Z
DTEND:20181026T203000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Dehan Kong (University of Toronto)
URL:/mathstat/channels/event/dehan-kong-university-tor
 onto-291019
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