Home

Welcome to the Section on Statistics in Defense and National Security

The purpose of this section is to provide a forum for statisticians working in the areas of defense, military research, national security, homeland security, and counterterrorism. This includes assisting in the advancement of knowledge in defense and national security by fostering wider and more effective use of statistical techniques, promoting the statistical profession and statistical best practices within the defense and national security governmental and affiliated organizations, encouraging professional review of statistical methods and statistical activities carried out in support of national security, particularly statistical research that is not available for public review, and disseminating information and providing training opportunities for individuals involved in the production and use of defense and national security data and statistics.


June SDNS Webinar

Dr. Christopher Franck, Associate Professor of the Department of Statistics at Virginia Tech presented his talk, An Introduction to Model Uncertainty and Averaging for Categorical Data Analysis, on Tuesday, 21 June 2022.  

Presentation can be found here (a separate Google Drive window will pop-up)
R Demo file can be found here (a separate Google Drive window will pop-up)


Abstract: Categorical data analysis is ubiquitous in the 21st century, and its analysis is vital to advance research in many domains. In an era with ever-expanding availability of data, the choice of which statistical model should be used is as important as ever. While statistical techniques to choose among competing models have been commonplace for a while, it seems that accessible techniques to effectively combine inferences over competitive models are not as widely used in practice. The purpose of this short course is to describe techniques that enable researchers to simultaneously leverage a variety of candidate models to improve prediction and inference. We describe an easy-to-use technique (based on the Bayesian information Criterion) to conduct approximate Bayesian model averaging, which weighs inferences proportionally to each candidate models’ posterior probability and can provide improved out-of-sample predictive performance over an individual model. We also discuss stacking, which combines model predictions according to each models’ out-of-sample predictive capability. Basic familiarity with logistic regression is a prerequisite for this course.

Learn more about the SDNS Webinar Series here! 


JSM 2022 banner


The statistical event of the summer—The Joint Statistical Meetings— will be held at the Walter E. Washington Convention Center in Washington, DC! Join us August 6–11 to meet new people, talk to old friends, and explore the nation’s capital.

With a focus on the 2022 theme, Statistics: A Foundation for Innovation, the JSM program consists not only of invited, topic-contributed, and contributed technical sessions, but also poster presentations, roundtable discussions, professional development courses and workshops, award ceremonies, and countless other meetings and activities.

Regular Registration ends June 30!


Recent Shared Files

No Data Found

Either the content you're seeking doesn't exist or it requires proper authentication before viewing.

Latest Discussions

There are no discussions to which you have access

Either the content you're seeking doesn't exist or it requires proper authentication before viewing.