BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20260621T060446EDT-2173SEC9tu@132.216.98.100
DTSTAMP:20260621T100446Z
DESCRIPTION:Singularities of the information matrix and longitudinal data w
 ith change points\n\n\n	Abstract:\n\n\nNon-singularity of the information m
 atrix plays a key role in model identification and the asymptotic theory o
 f statistics. For many statistical models\, however\, this condition seems
  virtually impossible to verify. An example of such models is a class of m
 ixture models associated with multi-path change-point problems (MCP) which
  can model longitudinal data with change points. The MCP models are simila
 r in nature to mixture-of-experts models in machine learning. The question
  then arises as to how often the non-singularity assumption of the informa
 tion matrix fails to hold. We show that\n\nthe set of singularities of the
  information matrix is a nowhere dense set\, i.e. geometrically negligible
 \, if the model is identifiable and some mild smoothness conditions hold. 
 Under further smoothness conditions we show that the set is also of measur
 e zero\, i.e. both geometrically and analytically negligible. In view of t
 hese results\, we further study class of semiparametric MCP models\, thus 
 paving the way for establishing asymptotic normality of the maximum likeli
 hood estimates (MLE) and statistical inference of the unknown parameters i
 n such models.\n\n\n	Speaker\n\n\nMasoud Asgharian is a Professor in the De
 partment of Mathematics and Statistics at 91ºÚÁÏÍø. His research 
 interest is on survival analysis\, change-point problems\, causal inferenc
 e\, and variable selection.\n
DTSTART:20190118T203000Z
DTEND:20190118T213000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Masoud Asgharian (91ºÚÁÏÍø)
URL:/mathstat/channels/event/masoud-asgharian-mcgill-u
 niversity-293312
END:VEVENT
END:VCALENDAR