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UID:20260415T075917EDT-90755FJ3v6@132.216.98.100
DTSTAMP:20260415T115917Z
DESCRIPTION:Title:\n\nDetection of Multiple Influential Observations on Var
 iable Selection for High-dimensional Data: New Perspective with an Applica
 tion to Neurologic Signature of Physical Pain.\n\nAbstract:\n\nInfluential
  diagnosis is an integral part of data analysis\, of which most existing m
 ethodological frameworks presume a deterministic submodel and are designed
  for low-dimensional data (i.e.\, the number of predictors p smaller than 
 the sample size n). However\, the stochastic selection of a submodel from 
 high-dimensional data where p exceeds n has become ubiquitous. Thus\, meth
 ods for identifying observations that could exert undue influence on the c
 hoice of a submodel can play an important role in this setting. To date\, 
 discussion of this topic has been limited\, falling short in two domains: 
 (1) constrained ability to detect multiple influential points\, and (2) ap
 plicability only in restrictive settings. In this talk\, building on a rec
 ently proposed measure\, we introduce a generalized version accommodating 
 different model selectors\, the asymptotic property of which is subsequent
 ly examined for large p. The K-means clustering is incorporated into our s
 cheme to detect multiple influential points. Simulation is then conducted 
 to assess the performances of various diagnostic approaches. The proposed 
 procedure further demonstrates its value in improving predictive power whe
 n analyzing thermal-stimulated pain based on fMRI data. In addition\, the 
 latest development revolving around this newly proposed measure is also pr
 esented. This work is conducted under the joint supervision of Professors 
 Masoud Asgharian and Martin Lindquist.\n\nSpeaker\n\nDongliang Zhang is a 
 PhD candidate in the Department of Biostatistics at the Bloomberg School o
 f Public Health\, Johns Hopkins University\, working under the joint super
 vision of Professors Martin Lindquist and Masoud Asgharian. Prior to his d
 octoral study\, he obtained his bachelor’s and master’s degrees respective
 ly in Honors Probability and Statistics\, and Mathematics and Statistics\,
  at the Department of Mathematics and Statistics\, McGill University. His 
 research interest revolves around large p small n problems with applicatio
 n to brain imaging data\, and he is a fan of Montreal Canadiens.\n
DTSTART:20230918T183000Z
DTEND:20230918T183000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Dongliang Zhang (Johns Hopkins University)
URL:https://www.mcgill.ca/mathstat/channels/event/dongliang-zhang-johns-hop
 kins-university-351020
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