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UID:20251123T034918EST-6330uWI581@132.216.98.100
DTSTAMP:20251123T084918Z
DESCRIPTION:MM Algorithms for Variance Components Models. \n\nVariance comp
 onents estimation and mixed model analysis are central themes in statistic
 s with applications in numerous scientific disciplines. Despite the best e
 fforts of generations of statisticians and numerical analysts\, maximum li
 kelihood estimation and restricted maximum likelihood estimation of varian
 ce component models remain numerically challenging. In this talk\, we pres
 ent a novel iterative algorithm for variance components estimation based o
 n the minorization-maximization (MM) principle. MM algorithm is trivial to
  implement and competitive on large data problems. The algorithm readily e
 xtends to more complicated problems such as linear mixed models\, multivar
 iate response models possibly with missing data\, maximum a posteriori est
 imation\, and penalized estimation. We demonstrate\, both numerically and 
 theoretically\, that it converges faster than the classical EM algorithm w
 hen the number of variance components is greater than two.\n\n \n\n \n
DTSTART:20170203T203000Z
DTEND:20170203T213000Z
LOCATION:room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Hua Zhou  (University of California)
URL:/mathstat/channels/event/hua-zhou-university-calif
 ornia-265502
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