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UID:20250918T054345EDT-9536VFNvmV@132.216.98.100
DTSTAMP:20250918T094345Z
DESCRIPTION:Title: Regularization in Finite Mixture of Sparse GLMs with Ult
 ra-High Dimensionality and Convergence of EM Algorithm\n\nAbstract: Finite
  mixture of generalized linear regression models (FM-GLM) are used for ana
 lyzing data that arise from populations with unobserved heterogeneity. In 
 recent applications of FM-GLM\, data are often collected on a large number
  of features. However\, fitting an FM-GLM to such high-dimensional data is
  numerically challenging. To cope with the high-dimensionality in estimati
 on\, it is often assumed that the model is sparse and only a handful of fe
 atures are relevant to the analysis. Most of the existing development on s
 parse estimation is in the context of homogeneous regression or supervised
  learning problems. In this talk\, I will discuss some of the challenges a
 nd recent computational and theoretical developments for sparse estimation
  in FM-GLM when the number of features can be in exponential order of the 
 sample size. Moreover\, I will discuss a modified EM algorithm to obtain t
 he estimates in FM-GLM numerically. The convergence theory of the modified
  EM algorithm for finite mixture of Gaussian regression with Lasso penalty
  will also be studied.\n
DTSTART:20231025T170000Z
DTEND:20231025T180000Z
LOCATION:Room 1214\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Pengqi Liu (9IÖÆ×÷³§Ãâ·Ñ)
URL:/mathstat/channels/event/pengqi-liu-mcgill-univers
 ity-352198
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