The National Program on
Complex Data Structures


at the University of Toronto, Medical Science Building
August 14 -17, 2006 -- 9 AM to 5 PM

Director: Paul N Corey
Co-directors: Jamie Stafford and Wendy Lou

Dr. Jamie Stafford
, Professor Public Health Sciences, Director of the National Program Complex Data Sets

.This session gives an introduction to a re-sampling method called the Bootstrap. Emphasis is placed on replacing mathematical rigor with computing in the context of both familiar and complex problems. Methods are motivated by the need to analyze data simply. An outline of material to be covered is as follows:

1. Motivation and a general framework: the sample mean and correlation coefficient.
2. Simple re-sampling is seemingly complex problems.
3. More complicated data structures.
4. Other uses of the bootstrap: from bias estimation to hypothesis testing and calibration

Recommended Text: Efron, B. and Tibshirani, R. (1993) An introduction to the bootstrap. Chapman and Hall, New York.

Dr. Jamie Stafford

Jamie Stafford is a Professor in, and the Associate Chair of, the Department of Public Health Sciences at the University of Toronto where he has his primary appointment. He is also an active member of the Department of Statistics where he has his secondary appointment. He received his PhD from the University of Toronto in 1992 and pursued postdoctoral studies at both Oxford and Stanford Universities. He held a faculty appointment at the University of Western Ontario before joining the University of Toronto in 1999. He has had numerous Visiting Professor appointments at prestigious institutions around the globe including the University of Chicago in 1997, Stanford University in 1999 and Ecole Polytechnique Federale de Lausanne in 1999. He is the current Director of the National Program on Complex Data Structures (NPCDS).

Jamie is responsible for groundbreaking research in the development of symbolic algorithms for statistical inference. The algorithms have tremendous impact on theoretical developments in statistics, as they are instrumental in eliminating 100's of pages of tedious algebra associated with contexts where lengthy derivations are required. By exploiting a common structure to such derivations the algorithms become quite general and are widely applicable to seemingly disparate areas of research. Jamie's research in this area ultimately led to the publication of a research monograph with Oxford University Press. Jamie has made other fundamental contributions to statistical foundations in robustness, survey methodology and missing data problems common in the health and environmental sciences.

He is a Fellow of the International Statistical Institute and a recent winner of the Premier's Research Excellence Award. He has served on many committees for the Statistical Society of Canada and other societies. For two years he played a major leadership role as Chair of the Grant Selection Committee for Statistics at NSERC.

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