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DTSTAMP:20260613T133905Z
DESCRIPTION:TITLE\n\nWhen likelihood goes wrong.\n\nABSTRACT\n\nInference b
 ased on the likelihood function is the workhorse of statistics\, and const
 ructing the likelihood function is often the first step in any detailed an
 alysis\, even for very complex data. At the same time\, statistical theory
  tells us that ‘black-box’ use of likelihood inference can be very sensiti
 ve to the dimension of the parameter space\, the structure of the paramete
 r space\, and measurement error in the data. This has been recognized for 
 a long time\, and many alternative approaches have been suggested with a v
 iew to preserving some of the virtues of likelihood inference while amelio
 rating some of the difficulties. In this talk I will discuss some of the w
 ays that likelihood inference can go wrong\, and some of the potential rem
 edies\, with particular emphasis on model misspecification.\n\nPLACE\n	Hybr
 ide - UQAM Salle / Room PK-R605\, Pavillon Président-Kennedy\n	\n	 \n\n\n	\n		
 \n			\n				Lien ZOOM Link\n			\n		\n	\n\n
DTSTART:20250425T193000Z
DTEND:20250425T203000Z
SUMMARY:Nancy Reid (University of Toronto)
URL:https://www.mcgill.ca/mathstat/channels/event/nancy-reid-university-tor
 onto-365042
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