Newsletter in Word
Election of Section Officers
Observational Medical Outcomes Partnership
Student Paper Competition
Joint Sectional Meeting, Nonparametric Statistics & SLDM, May 19-22
Call for Papers, Statistical Analysis and Data Mining
1. Election of Section Officers
It is now time to provide a slate of candidates for our Section for the ASA
2010 election. Two positions are filled through election, and all others are
appointed by the Chair. The elected offices are
Chair-Elect     Program Chair-Elect
The nominations are due by November 30, with the candidates' bios then due
by January 15. However, please keep in mind that the earlier the nominations
are in the more time the candidates will have to submit their bios and/or
make changes to them. Also please remember that all activities regarding the
ASA election process are handled by an outside vendor except for receiving
nominations and bios so every effort must be made to make the assigned
deadlines. In addition ASA elections always have been and continue to be
contested elections so at least two candidates are needed for each elected
Please submit nominations and or questions to
Xiaotong Shen, firstname.lastname@example.org
When you submit your nominations please remember the following:
Should your prospective candidates need further information regarding the
position they are being asked to run for you may want to refer them to our
Section charter online at
To check the spelling of the individuals name and have their official name.
Have their latest affiliation in full.
Confirm with the individual that they belong to our Section and have renewed
The Observational Medical Outcomes Partnership (OMOP) seeks new
statistical and data mining methods for detecting drug safety issues through
the OMOP Cup Methods Competition.
Observational data analyses algorithms are routinely used throughout the
world to monitor the safety of prescription drugs. With the increasing
attention and implementation of electronic healthcare information, new and
improved analysis approaches are urgently needed. The OMOP Cup is your
opportunity to contribute new methods to monitor drug safety.
The OMOP Cup provides a simulated dataset of 10 million hypothetical patients
with drug and condition information. Known drug safety issues are present in
the simulated dataset and your charge is to effectively and efficiently
identify these issues. The OMOP Cup has two challenges:
Challenge 1: Explores how well methods work when against an entire dataset, targeting the accurate classification of which drugs are associated with which outcomes.
Challenge 2: Evaluates the timeliness of detection of drug-event associations by having methods run against data sequentially as it accumulates over time.
The total prize money is $20,000. Winners will be required to place their
algorithms in the public domain.
Details about the OMOP Cup Methods Competition can be found at:
The competition is being conducted by the Observational Medical Outcomes
Partnership (http://omop.fnih.org), a
public-private research partnership of the Foundation for the National
Institutes of Health.
2b. SLDM Student Paper Competition
The SLDM section is sponsoring a Student Paper Competition for JSM
2010. Students are encouraged to submit a paper which might be original
methodological research or analysis of data(from various fields including
but not limited to pharmacy, genomics, bioinformatics, imaging, defense,
business, public health) that uses principles and methods in statistical
learning and data mining.
Submission details are posted on our section website:
All application materials must be submitted electronically (pdf files are
preferred) and must be received by 11 PM EST, Friday, December 18, 2009 to:
email@example.com. Award announcements will be made in late January 2010.
3. Joint Sectional Meeting
The SLM and Nonparametric Statistics Sections, together with the Department
of Statistics at The Ohio State University, under sponsorship of the National
Science Foundation, are holding a Nonparametric Statistics and Statistical
Learning Conference in Columbus, May 19-22, 2010. Its purpose is to bring
together researchers from academia, industry, and government in a stimulating
atmosphere to focus on principles and methods that apply to both disciplines
and to promote the exchange of ideas between them. Topics include, but are
not limited to, the areas of distribution free statistics, rank-based and
robust statistics including data depth measures, Bayesian nonparametric
methods, permutation-based methods, nonparametric regression and density
estimation, multivariate statistics, data mining, and statistical learning.
On-line registration begins Nov 2. See the conference website
http://conference.stat.osu.edu/nssl2010/) for more details.
Prior to the actual conference, Prof. Gary Koch will be delivering the annual
Rustagi Lecture at OSU at 3:30 p.m. All conference participants are welcome
to attend the lecture and ensuing reception (4:30-7:00) in the 11th Floor
Reading Room of the newly renovated Main Library.
4. Call for Papers, Statistical Analysis and Data Mining(SAM)
Lynne Billard, Department of Statistics, University of Georgia, GA, USA
(firstname.lastname@example.org) is guest editing a Special Issue on Symbolic Data
Analysis (SDA) With the advent of modern computing, databases are becoming so
large or complex that classical statistical methods are often not able to
analyze them in practice, due to either their size or their complexity. New
methods are being developed to handle these databases, and many are on the
interface of Statistical Analysis and Data Mining. One such area is Symbolic
Data Analysis. Although symbolic data may arise naturally, they are
increasingly important as a result of aggregating large to massive
databases. Those databases will naturally contain symbolically-valued data:
such as lists, intervals, histograms or distributions. Further, different
scientifi c questions that are asked concerning a particular aggregation may
well produce different symbolic_databases. Since symbolic data have internal
variations (that do not exist in classical data) as well as
between-observations variations (in both classical and symbolic data),
classical statistical methods applied to symbolic data may yield results that
are often inadequate or misleading. Therefore, new methods are being
developed to handle symbolic data. Papers should present new results in
symbolic data analysis: such as methods for new classes of symbolic data,
insights from applications in new areas, or theoretical underpinnings for
current methods. The deadline for submission is January 19, 2010.
In addition to this Special Issue, we are soliciting papers in the general
areas of Statistical Learning and Data Mining. For further information, see
the journal's website
or contact Joe Verducci (email@example.com) with questions or proposals.
Note: ASA members have free access to SAM through the end of 2010; after that a special rate will apply.