Speaker: Nancy Reid, University Professor of Statistical Sciences, University of Toronto

Title: Likelihood and its Discontents

Abstract: While there may have been a time when inference based on the likelihood function was too difficult to implement, advances in computing have long since overcome this, and the likelihood function provides a reliable foundation for Bayesian and non-Bayesian analyses in a wide range of applications. The formal justification is largely through asymptotic arguments, under “the usual regularity conditions”. With the increase in complexity of models and size of data, computational and theoretical problems arise anew, and many ingenious extensions to the likelihood function and to asymptotic theory have been developed in response. This talk gives an overview of some of the recent developments that seem to me interesting and challenging, for both statistical theory and practice.