**Take a sneak peek at the new NIST.gov and let us know what you think!***(Please note: some content may not be complete on the beta site.)*.

NIST Home > ITL > Statistical Engineering Division > SED Archives > Research on Statistical Methods Project

## Summary:
Since the formation of the Statistical Engineering Division in 1947, division staff, through their interdisciplinary research with NIST scientists and engineers, occasionally encounter problems that cannot be addressed using existing, or textbook, statistical methods. On such occasions, appropriate division staff conduct original research in mathematical and/or computational statistics, leading to new and more broadly applicable statistical methods. The division's unique contributions to the general methods of statistics tend to concentrate in areas where the measurement science activities at NIST present new challenges in planning and analyzing high precision data on high-accuracy measurement systems. So, many of the divisions original contributions fall into the following areas:
- Bayesian methods for metrology,
- statistical calibration and measurement assurance,
- experiment designs,
- components-of-variance estimation,
- methods for the design and analysis of interlaboratory comparisons, and
- measurement process control. The division also conducts limited research in some areas enabled by modern computing systems:
- computer intensive methods (bootstrap, permutation procedures, general distribution-free methods) and
- image analysis methods.
## Description:
The division typically produces one to a few publications on new statistical methods each year. A list of publications on some research from the past few years is shown below below. Following the publication list, an example of current work on the development of novel statistical methods, useful both at NIST and elsewhere, is presented in more detail.
## Major Accomplishments:
The following are research publications written by SED staff and collaborators.
- Zhang, N. F. (2000). "Statistical Control Charts for Monitoring the Mean of a Stationary Process", to appear in the Journal of Statistical Computation and Simulation. |
## Lead Organizational Unit:itl## Customers/Contributors/Collaborators:
Visiting Guest Researchers and Fellows from academia.
## Staff:
Various staff of the Statistical Engineering Division, including faculty appointees from the the University of Maryland, U. of Maryland Baltimore County, Colorado State University.
Contact
Antonio Possolo |