SLDM 2009 Autumn Newsletter

Newsletter in Word


  1. Election of Section Officers
  2. Contests
    1. Observational Medical Outcomes Partnership
    2. Student Paper Competition
  3. Joint Sectional Meeting, Nonparametric Statistics & SLDM, May 19-22
  4. 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 position.

Please submit nominations and or questions to

Xiaotong Shen,

When you submit your nominations please remember the following:

  1. To check the spelling of the individuals name and have their official name.
  2. Have their latest affiliation in full.
  3. Confirm with the individual that they belong to our Section and have renewed their membership.
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

2. Contests

  • 2a. 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 (, 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: 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 ( 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 ( 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 ( with questions or proposals.

Note: ASA members have free access to SAM through the end of 2010; after that a special rate will apply.