ADVANCE Speaker Series and the Department of Statistics Proudly Presents
Dr. Nancy Reid 
University Professor of Statistical Sciences
Canada Research Chair in Statistical Methodology
University of Toronto
“Approximate Likelihoods”
In complex models likelihood functions may be difficult to compute, or depend on assumptions about high order dependencies that may be difficult to verify. A number of methods have been devised to compute inference functions either meant to approximate the true likelihood function, or to provide inferential summaries that balance statistical efficiency with ease of computation. Examples include variational approximations, composite likelihood, quasi-likelihood, indirect inference, and Laplace-type approximations. This talk will survey various approximations to likelihood and likelihood inference, with a view to identifying common themes and outstanding problems.
Reception to follow at the University Club at 5:00 pm.
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BIO
Nancy Reid is University Professor and Canada Research Chair in Statistical Methodology at the University of Toronto. Her research interests are in statistical theory, likelihood inference, and design of studies. Along with her colleagues she has developed higher order asymptotic methods both for use in applications, and as a means to study theoretical aspects of the foundations of inference, including the interface between Bayesian and frequentist methods.
Professor Reid received her PhD from Stanford University, under the supervision of Rupert Miller. She taught at the University of British Columbia before moving to the University of Toronto, and has held visiting positions at the Harvard School of Public Health, University of Texas at Austin, Ecole Polytechnique Federale de Lausanne, and University College London. In 2009 she received the Emanuel and Carol Parzen Prize for statistical innovation, and the Gold Medal of the Statistical Society of Canada.
She is a Fellow of the Royal Society of Canada, the American Association for the Advancement of Science, the Institute of Mathematical Statistics and the American Statistical Association.
