Nancy Reid (University of Toronto)
TITLE
When likelihood goes wrong.
ABSTRACT
Inference based on the likelihood function is the workhorse of statistics, and constructing the likelihood function is often the first step in any detailed analysis, even for very complex data. At the same time, statistical theory tells us that 鈥榖lack-box鈥� use of likelihood inference can be very sensitive to the dimension of the parameter space, the structure of the parameter space, and measurement error in the data. This has been recognized for a long time, and many alternative approaches have been suggested with a view to preserving some of the virtues of likelihood inference while ameliorating some of the difficulties. In this talk I will discuss some of the ways that likelihood inference can go wrong, and some of the potential remedies, with particular emphasis on model misspecification.
PLACE
Hybride - UQAM Salle / Room PK-R605, Pavillon Pr茅sident-Kennedy
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