Featured Speakers and Named Lectures

ASA President’s Address and Founder & Fellows Recognition

Lisa LaVange
The University of North Carolina
Tuesday, July 31, 8:00 p.m.

ASA Deming Lecture

John L. Eltinge
US Census Bureau
“Improving the Quality and Value of Statistical Information: Fourteen Questions on Management”
Tuesday, July 31, 4:00 p.m.

In his landmark book, Out of the Crisis, W. Edwards Deming presented “Fourteen Points for Management.” Taken as a whole, his points reflected a profoundly humane and nuanced perspective on improved management of large and complex organizations. The exposition of those 14 points centered primarily on management of quality and productivity in manufacturing, but also was informed by Deming’s extensive experience with sample surveys. In addition, his commentary on these points included substantial discussion of work by government agencies and other organizations that produce statistical information for public use.

COPSS Awards and Fisher Lecture

Susan Murphy
Harvard University
“The Future: Stratified
Micro-Randomized Trials with Applications in Mobile Health”
Wednesday, August 1, 4:00 p.m.

Technological advancements in the field of mobile devices and wearable sensors make it possible to deliver treatments anytime and anywhere to users like you and me. Increasingly, the delivery of these treatments is triggered by detections/predictions of vulnerability and receptivity. These observations are likely to have been affected by prior treatments. Furthermore, the treatments are often designed to have an impact on users over a span of time during which subsequent treatments may be provided.

Here, I discuss work on the design of a mobile health smoking cessation study in which the above two challenges arose. This work involves the use of multiple online data analysis algorithms. Online algorithms are used in the detection, for example, of physiological stress. Other algorithms are used to forecast, at each vulnerable time, the remaining number of vulnerable times in the day. These algorithms are then inputs into a randomization algorithm that ensures each user is randomized to each treatment an appropriate number of times per day.

The stratified micro-randomized trial that involves not only the randomization algorithm, but a precise statement of the meaning of the treatment effects and primary scientific hypotheses, along with primary analyses and sample size calculations, is developed. Considerations of causal inference and potential causal bias incurred by inappropriate data analyses play a large role throughout.

IMS Medallion Lecture I

Anthony Davison
EPFL
Statistical Inference for Complex Extreme Events
Sunday, July 29, 2:00 p.m.

Statistics of extremes deals with the estimation of events that have low probabilities but potentially large consequences, such as stock market gyrations, flooding, and heat waves. Often, the events of interest have never been observed, and hence their probabilities must be estimated by extrapolation well outside any existing data. This area exhibits a beautiful interplay between probability and statistics and has a long history and rich tradition of applications, originally in insurance and engineering, but increasingly in the environmental sciences and in finance. Statistical methods for modeling scalar and multivariate extremes based on sample maxima and threshold exceedances are well-established, but the focus has turned to more complex settings, including spatial and space-time modeling of extremal phenomena. In this lecture, I shall survey recent work on the topic and then show how detailed modeling for high-dimensional problems can be undertaken using Pareto processes, generalized versions of threshold exceedances, and gradient scoring rules.

IMS Medallion Lecture II

Ming Yuan
Columbia University
“Statistical Analysis of Large Tensors”
Wednesday, August 1, 2:00 p.m.

A large amount of multidimensional data in the form of multilinear arrays, or tensors, arise routinely in modern applications from such diverse fields as chemometrics, genomics, physics, psychology, and signal processing. At the moment, our ability to generate and acquire them has far outpaced our ability to effectively extract useful information from them. There is a clear demand to develop novel statistical methods, efficient computational algorithms, and fundamental mathematical theory to analyze and exploit information in these types of data. In this talk, I will review recent progress and discuss some of the present challenges.

Public Lecture

Jeffrey Rosenthal
University of Toronto
“Born on Friday the Thirteenth: The Curious World of Probabilities”
Monday, July 30, 7:00 p.m.

This talk will use randomness and probability to answer questions such as the following: Just how unlikely is it to win a lottery jackpot? If you flip 100 coins, how close will the number of heads be to 50? How many dying patients must be saved to demonstrate the effectiveness of a new medical drug? Why do strange coincidences occur so often? How accurate are opinion polls? How did statistics help expose the Ontario Lottery Retailer Scandal? Should parents be convicted of murder if two of their babies die without apparent cause? Can statistics explain luck and superstition? Why do casinos always make money, even though gamblers sometimes win? And how is all of this related to Monte Carlo algorithms, an extremely popular and effective method for scientific computing? No mathematical background is required to attend.