Professor William Schucany became known as “Mr. Statistics” during his 40-year career with SMU. By the time he retired in 2011, he was called the heart and soul of the department. And even as an emeritus faculty member, he enjoys coming back to the Hilltop for a good seminar presentation.
To honor Schucany, the Department of Statistical Science has created the Bill Schucany Scholar Lecture Series, which will bring elite statisticians from around the world to SMU. Bradley Efron, Max H. Stein Professor of Statistics and Biostatistics at Stanford University and innovator of bootstrap technology, will be the inaugural presenter in two events scheduled for Thursday and Friday, Feb. 27-28, 2014.
“When people thought of our department, they thought of Bill Schucany,” says longtime colleague Wayne Woodward, professor and chair of the Department of Statistical Science in Dedman College of Humanities and Sciences. “Bill loves seminars – I think our Friday seminars were his favorite time of the week. We wanted to honor him, and we chose this as the thing that would mean the most to him.”
Schucany’s former students provided much of the series’ funding, which has been supplemented with departmental funds. Students, alumni and faculty members are all invited to attend the event, which the department plans to make an annual meeting.
The series’ first speaker is widely regarded as one of the most influential statisticians of all time, Woodward says. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Efron received the National Medal of Science for his contributions to the discipline, notably his innovation of the bootstrap technique. The method uses relatively simple yet computationally intensive techniques to produce accurate statistical estimates from very small random samples. Bootstrapping and its related computational techniques have greatly expanded the scope of statistical analyses, especially in the current environment of massive databases and computing capabilities.
“Anyone who would rank the top five statisticians in the world would include Brad Efron,” Woodward says. “He is an elite scholar who graciously accepted our invitation, probably because he holds Bill in such esteem. His Thursday evening talk is intended for a general scientific audience, and we encourage anyone with an interest to attend either or both events.”
Schucany himself is internationally recognized for his contributions to the field of nonparametric statistical inference. As a Fellow of the American Statistical Association (ASA), he was chosen in 2004 as one of only four ASA members to received its Founder’s Award, the highest honor the association bestows for service to the profession. Among numerous other honors, Schucany has received the national Don Owen Research Award from the San Antonio Chapter of the ASA and the Paul Minton Award from the Southern Regional Council on Statistics. In addition, he was elected to membership in the International Statistical Institute. Schucany has also served as editor of The American Statistician and as associate editor of the Journal of the American Statistical Association, the Journal of Educational Statistics, and Communications in Statistics.
Efron will give two public lectures during his SMU visit:
- “Learning from the Experience of Others” – 7 p.m. Thursday, Feb. 27, location TBA. Familiar statistical estimates such as batting averages, political polls and medical trial results are obtained by direct observation of cases of interest. Sometimes, though, we can learn from the experience of “others” – e.g., there may be information about one player’s batting average in the observed averages of other players. Efron will present several examples showing how this works in practice, indicating some of the surprising theoretical ideas involved. The talk is intended for a general scientific audience.
- “Frequentist Accuracy of Bayesian Estimates” – 3 p.m. Friday, Feb. 28, location TBA. In the absence of prior information, popular Bayesian estimation techniques usually begin with some form of “uninformative” prior, intended to have minimal inferential influence. Bayes rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to genuine priors, and require further justification. This talk concerns computational formulas that produce frequentist accuracy assessments for Bayesian estimates. Both encouraging and cautionary examples will be presented.