QREI Special Issue on Advancing Statistical Methods for Testing and Evaluating Defense Systems

Quality and Reliability Engineering International has published a special issue on Advancing Statistical Methods for Testing and Evaluating Defense Systems (37:7). The special issue was guest edited by Rebecca M. Medlin of the Institute for Defense Analyses. Edited portions of Dr Medlin’s Editorial along with a list of papers in the special issue are included below.

It was an honor to edit this special issue of QREI, which showcases research from the Science of Test Research Consortium. The Science of Test Research Consortium, is a consortium of institutions dedicated to advancing statistical approaches in the testing and evaluation of military systems.

Research focus areas of the consortium include experimental design, regression analysis, functional data analysis, reliability modeling and analysis, applications of Bayesian methods to T&E, validation of computer models and simulations, and issues associated with testing in a live, virtual, and constructive environments.

The DoD acquires highly complex systems that must be tested and evaluated before they are fielding to military users and placed in actual operations. The statistical challenges are vast. Analysis challenges include non-normal, messy, and missing data, function outcomes, limited data, and data from various types of activities that needs to be combined into a single evaluation. Additionally, these tests are expensive and are conducted on ranges with limited availability. The systems are operated by military users and their time is valuable and hard to acquire. For all of these reasons, every data point that is requested from a test is scrutinized and evaluated.

The articles in this journal highlight a collection of recent research from the consortium and other T&E researchers. The articles showcase advances in experimental design to improve test efficiency; Bayesian methods for modeling reliability growth; change point detection and estimations of functional observations; and a case study application using Bayesian techniques to model miss distance of a ballistic projectile.

A unique aspect of this special issues of the journal is that we included reviewers from both the statistical academic community and those practicing statistics in the DoD. I am grateful for their time and the suggestions they provided to each of our authors, which further improved this special issue. The result is a diversity of research that showcases the depth and breadth of statistical challenges faced in the Defense context.

Circular prediction regions for miss distance models under heteroskedasticity

Nonlinear profile monitoring with single index models

A framework for improving the efficiency of operational testing through Bayesian adaptive design

Optimal designs for dual response systems for the normal and binomial case

Aliased informed model selection strategies for six-factor no-confounding designs

A compound optimality criterion for 𝐷D-efficient and separation-robust designs for the logistic regression model

Gradient-based criteria for sequential experiment design

Practical reliability growth modeling

Analysis of correlated multivariate degradation data in accelerated reliability growth

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