Nancy Reid is a statistician recognized for her work on the theory of statistical inference, with an emphasis on likelihood-based methods and higher order asymptotics. She is known particularly for her development with D.R. Cox on adjustments to profile likelihood, and her work with D.A.S. Fraser on highly accurate approximations to significance functions. Reid was born in Niagara Falls, Canada and obtained her B.Math from the University of Waterloo, M.Sc. from the University of British Columbia and PhD from Stanford University in 1974. Following a postdoctoral fellowship at Imperial College London she taught at the University of British Columbia, moving to the University of Toronto in 1986, where she is University Professor of Statistics and Canada Research Chair. She has been president of the Institute of Mathematical Statistics and of the Statistical Society of Canada, and in 2014 became the Director of the Canadian Institute for Statistical Sciences. She is Fellow of the Royal Society of Canada, Officer of the Order of Canada and Foreign Associate of the National Academy of Sciences.

Research Interests

Nancy Reid's research emphasizes the use of asymptotic expansions to suggest more accurate approximations to standard statistical quantities, both for use in applications, and as a means to study theoretical aspects of the foundations of inference. This research has shed light on the interface between Bayesian and frequentist methods, showing in particular that except in special cases, Bayesian and frequentist inference cannot agree exactly, unless one permits the use of data-dependent priors. Her current interests include the use of various types of pseudo-likelihoods for inference from data with complex structure, particularly the use of composite likelihood for simplifying inference in models with potentially complex dependencies. She and her students and colleagues are studying both the theoretical properties of various proposals, and the interconnections among them. Her applied areas of interest include environmental epidemiology, design of experiments, and inference from large-scale surveys. She has a long-standing interest in conveying statistical arguments to the lay public, and the importance of using data to inform public policy.

Membership Type

International Member

Election Year


Primary Section

Section 32: Applied Mathematical Sciences