zbMATH — the first resource for mathematics

Statistical analysis for nuclear forensics experiments. (English) Zbl 07260451
Summary: As with any type of forensics, nuclear forensics seeks to infer historical information using models and data. This article connects nuclear forensics and calibration. We present statistical analyses of a calibration experiment that connect several responses to the associated set of input values and then ‘make a measurement’ using the calibration model. Previous and upcoming real experiments involving production of \(PuO_2\) powder motivate this article. Both frequentist and Bayesian approaches are considered, and we report findings from a simulation study that compares different analysis methods for different underlying responses between inputs and responses, different numbers of responses, different amounts of natural variability, and replicated or non-replicated calibration experiments and new measurements.
62 Statistics
68 Computer science
BayesDA; R; WinBUGS
Full Text: DOI
[1] R. Krutchkoff, Classical and inverse regression methods of calibration, Technometrics 9 (1967), 425-439.
[2] R. Krutchkoff, Classical and inverse regression methods of calibration in extraplolation, Technometrics 11 (1969), 605-608. · Zbl 0179.48903
[3] H. Martens and T. Naes, Multivariate Calibration, New York, NY, John Wiley & Sons, Inc., 1987. · Zbl 0732.62109
[4] W. A. Fuller, Measurement Error Models, New York, NY, John Wiley & Sons, Inc., 1987. · Zbl 0800.62413
[5] G. A. Burney and P. K. Smith, Controlled PuO Particle Size from PU(III) Oxalate Precipitation. Savannah River Laboratory, Technical Report DP-1689, 1984.
[6] R Development Core Team, R: a Language and Environment for Statistical Computing, Vienna, R Foundation for Statistical Computing, 2004, http://www.R-project.org
[7] M. Hamada, A. Pohl, C. Spiegelman, and J. Wendelberger, A Bayesian approach to calibration intervals and properly calibrated tolerance intervals, J Qual Technol 35 (2003), 194-205.
[8] G. Casella, E. I. George, Explaining the Gibbs sampler, Am Stat 46 (1992), 327-335.
[9] S. Chib, E. Greenberg, Understanding the Metropolis-Hastings algorithm, Am Stat 49 (1995), 327-335.
[10] A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin, Bayesian Data Analysis (2nd ed.), Boca Raton, Chapman and Hall, 2003. · Zbl 1039.62018
[11] D. Spiegelhalter, A. Thomas, N. Best, D. Lunn, WinBUGS Version 1.4.3 User Manual, Cambridge, UK, MRC Biostatistics Unit,2010, http://www.mrc-bsu.cam.ac.uk/ software/bugs/winbugs/contents.shtml
[12] M. D. McKay, R. J. Beckman, and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21 (1979), 239-245. · Zbl 0415.62011
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.