Section Readies for 2010 Conferences
Will Guthrie, JRC Organizing Committee
The 17th Spring Research Conference on Statistics in Industry and Technology and the 27th Quality and Productivity Research Conference will be held jointly at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland, May 25–27.
The guest of honor will be Vijay Nair of the University of Michigan. He will be recognized for his many contributions to the practice of industrial statistics and service to the statistical community. Other plenary speakers include Stephen Fienberg from Carnegie Mellon University, speaking on opportunities for statisticians to contribute to forensic science; Bradley Jones of JMP, giving a talk titled “Designed Experiments That Changed the World”; and Diane Lambert of Google, presenting work on an automated system for measuring the effectiveness of web display ad campaigns from observational data.
The invited program includes 18 sessions on experiment design, process control, reliability, and other topics of interest to statisticians in the physical and engineering sciences. This year’s “Randy Sitter Technometrics Session” was organized by David Steinberg of Tel Aviv University, and the speakers will be Haipeng Shen from the University of North Carolina and Adrian Raftery from the University of Washington. Shen’s talk is titled “On Modeling and Forecasting Time Series of Smooth Curves,” and Raftery will present a talk discussing “Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill.”
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Kary Myers, SPES Program Chair-elect
SPES is pleased to sponsor three roundtables at the 2010 JSM covering an excellent collection of topics:
Christine M. Anderson-Cook of Los Alamos National Laboratory will bring her insights to a discussion on “Balancing Competing Objectives for a Good Designed Experiment,” in which she considers the practical trade-offs involved between trusting our knowledge of a system and recognizing that we might not have correctly or completely captured the characteristics of the system. This roundtable will start at 12:30 p.m.
Alexander Kolovos of SAS Institute will consider stochastic modeling of solar radiation and wind fields when he leads a discussion of “Innovations in Spatiotemporal Analysis for Renewable Energy Research.” This roundtable will provide a venue in which participants can share related methodologies and facilitate a connection between academia and industry. The roundtable will start at 7 a.m.
Dan Nordman of Iowa State will lead a roundtable and share his experience with “Orientation Data Analysis in Physical Sciences,” including development of models for directional data (like 2-d wind directions) and for 3×3 rotation matrices (like crystal orientations in metal surfaces). The discussion will range from the science that generates orientation data to the statistical methodologies that have been applied to these problems. The roundtable will start at 7 a.m.
Check out the JSM 2010 online program for more detailed descriptions of the roundtable topics. Each roundtable is limited to 10 people, so please register early. Note that SPES has some scholarships available for students to attend one of these SPES-sponsored roundtables; contact Kary Myers at firstname.lastname@example.org.
SPES Short Course
Tena Katsaounis, SPES Education Chair
If you are attending the Joint Statistical Meetings this year, mark your calendar for the short course “Monte Carlo and Bayesian Computation with R,” by Jim Albert and Maria Rizzo. This course was offered successfully at the 2009 JSM, and SPES is excited to sponsor it again this year. Details about the course will be posted on ASA’s web site under sections and meetings.
Albert and Rizzo are professors in the department of mathematics and statistics at Bowling Green State University. Albert has written several texts on Bayesian modeling and computation. He has taught short courses on ordinal data modeling at JSM (with Val Johnson) and on the use of sports in teaching statistics. Rizzo regularly teaches a doctoral-level course in statistical computing and has recently published a text on statistical computing using R.
This course is intended for statisticians who are interested in using the R system to design Monte Carlo experiments to assess the properties of statistical procedures. Also, the course is helpful for those who wish to learn about the use of R as an environment for Bayesian computations.