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UID:20251121T113303EST-90675E733W@132.216.98.100
DTSTAMP:20251121T163303Z
DESCRIPTION:The role of tuning parameters in high dimensional problems.\n\n
 Various forms of penalty functions have been developed for regularized est
 imation. Screening approaches are often used to reduce the number of covar
 iate before penalized estimation. However\, in certain problems\, the numb
 er of covariates remains large after screening. For example\, in genome-wi
 de association studies\, the purpose is to identify Single Nucleotide Poly
 morphisms (SNPs) that are associated with certain traits. Because of the s
 trong correlation of nearby SNPs\, screening can only reduce the number of
  SNPs from millions to tens of thousands. Several penalty functions have b
 een proposed for such high dimensional data. However\, it is unclear which
  class of penalty functions is the appropriate choice for a particular app
 lication. In this talk\, I will discuss the results of the theoretical ana
 lysis to relate the ranges of tuning parameters of various penalty functio
 ns with the dimensionality of the problem and the minimum effect size.\n
DTSTART:20170316T193000Z
DTEND:20170316T203000Z
LOCATION:room D4-2019\, CA\, QC\, Sherbrooke\, Seminar Statistique Sherbroo
 ke\, 2500 Boul de L'Université
SUMMARY:Ting-Huei Chen\, Université Laval
URL:/mathstat/channels/event/ting-huei-chen-universite
 -laval-266913
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