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DTSTAMP:20250807T195203Z
DESCRIPTION:Title: Feature Selection for Linear Fixed Effects Models\n\n\n	A
 bstract:\n\nLinear mixed-effects (LME) models are used to analyze nested o
 r combined data across a range of groups or clusters. These models use cov
 ariates to separate the total population variability (the fixed effects) f
 rom the group variability (the random effects). LMEs borrow strength acros
 s groups to estimate key statistics in cases where the data within groups 
 may be sparse or highly variable\, and play a fundamental role in populati
 on health sciences\, meta-analysis\, life sciences\, and in many others do
 mains. In this talk we formally introduce a mathematical description of th
 e LME model and its feature selection variant. A naive proximal gradient d
 escent (PGD) algorithm for its solution is described and its deficiencies 
 are explained. A novel solution strategy is proposed that is based on rela
 xation strategy that decouples the smooth from the nonsmooth components of
  the maximum likelihood objective. An optimal value function is obtained b
 y partially optimizing the smooth component of the decoupled problem. We s
 how that the resulting optimal value function has a locally Lipschitz grad
 ient and so a PGD algorithm can be applied to a feature selecting regulari
 zation of the optimal value function. At first this approach seems counter
  intuitive since the optimal value function adds yet another layer of comp
 lexity to the problem. However\, this complexity is mitigated by the use o
 f modern variational and numerical techniques. The resulting PGD algorithm
  applied to this reformulation is more stable and can rapidly identify the
  important features to high accuracy. The algorithmic details and the nume
 rical results are presented.\n
DTSTART:20231113T210000Z
DTEND:20231113T220000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:James V. Burke (University of Washington )
URL:/mathstat/channels/event/james-v-burke-university-
 washington-352634
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