Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Lewis, John R.; Zhang, Adah; Anderson-Cook, Christine Michaela

doi:10.1016/j.chemolab.2017.10.010

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Journal Article · Sun Oct 29 00:00:00 EDT 2017 · Chemometrics and Intelligent Laboratory Systems

DOI:https://doi.org/10.1016/j.chemolab.2017.10.010· OSTI ID:1415409

Lewis, John R. ^[1]; Zhang, Adah ^[1]; ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors (X) of some underlying causal model producing the observables or responses (Y = g (X) + error). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research in which inverse predictions, along with an assessment of predictive capability, are desired.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL)

Sponsoring Organization:: U.S. Department of Homeland Security; USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1415409

Report Number(s):: LA-UR-17-25139

Journal Information:: Chemometrics and Intelligent Laboratory Systems, Journal Name: Chemometrics and Intelligent Laboratory Systems Vol. 175; ISSN 0169-7439

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (19)

Asymptotic efficiency properties of least squares in an ultrastructural model Srivastava, A. K. Test, Vol. 6, Issue 2, p. 419-431 https://doi.org/10.1007/BF02564707	journal	December 1997
Least squares estimators in measurement error models under the balanced loss function No authors listed Test, Vol. 10, Issue 2, p. 301-308 https://doi.org/10.1007/BF02595699	journal	December 2001
Inverse vs. classical calibration for small data sets Tellinghuisen, J. Fresenius' Journal of Analytical Chemistry, Vol. 368, Issue 6 https://doi.org/10.1007/s002160000556	journal	November 2000
Inverse calibration predicts better than classical calibration Centner, V.; Massart, D. L.; de Jong, S. Fresenius' Journal of Analytical Chemistry, Vol. 361, Issue 1 https://doi.org/10.1007/s002160050825	journal	April 1998
Analyzing censored data in the analysis of multiplicative repeatability and reproducibility studies Burr, T.; Hamada, M. S. Accreditation and Quality Assurance, Vol. 19, Issue 2 https://doi.org/10.1007/s00769-014-1044-9	journal	March 2014
Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements Krasnopolsky, Vladimir M.; Schiller, Helmut Neural Networks, Vol. 16, Issue 3-4 https://doi.org/10.1016/S0893-6080(03)00027-3	journal	April 2003
Design of experiments and data analysis challenges in calibration for forensics applications Anderson-Cook, Christine; Burr, Tom; Hamada, Michael S. Chemometrics and Intelligent Laboratory Systems, Vol. 149 https://doi.org/10.1016/j.chemolab.2015.07.008	journal	December 2015
Response surface design evaluation and comparison Anderson-Cook, Christine M.; Borror, Connie M.; Montgomery, Douglas C. Journal of Statistical Planning and Inference, Vol. 139, Issue 2 https://doi.org/10.1016/j.jspi.2008.04.004	journal	February 2009
Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information Haaland, David M.; Thomas, Edward V. Analytical Chemistry, Vol. 60, Issue 11 https://doi.org/10.1021/ac00162a020	journal	June 1988
The Prediction Properties of Classical and Inverse Regression for the Simple Linear Calibration Problem Parker, Peter A.; Vining, G. Geoffrey; Wilson, Sara R. Journal of Quality Technology, Vol. 42, Issue 4 https://doi.org/10.1080/00224065.2010.11917831	journal	October 2010
Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M. Journal of Quality Technology, Vol. 49, Issue 3 https://doi.org/10.1080/00224065.2017.11917992	journal	July 2017
Classical and Inverse Regression Methods of Calibration Krutchkoff, R. G. Technometrics, Vol. 9, Issue 3 https://doi.org/10.1080/00401706.1967.10490486	journal	August 1967
Classical and Inverse Regression Methods of Calibration in Extrapolation Krutchkoff, Richard G. Technometrics, Vol. 11, Issue 3 https://doi.org/10.1080/00401706.1969.10490714	journal	August 1969
A Comparison of Classical and Inverse Estimators in the Calibration Problem Kannan, Nandini; Keating, Jerome; Mason, Robert Communications in Statistics - Theory and Methods, Vol. 36, Issue 1 https://doi.org/10.1080/03610920600966225	journal	January 2007
Predicting Multivariate Responses in Multiple Linear Regression Breiman, Leo; Friedman, Jerome H. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 59, Issue 1 https://doi.org/10.1111/1467-9868.00054	journal	February 1997
Bayesian calibration of computer models Kennedy, Marc C.; O'Hagan, Anthony Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3 https://doi.org/10.1111/1467-9868.00294	journal	August 2001
Inference from Iterative Simulation Using Multiple Sequences Gelman, Andrew; Rubin, Donald B. Statistical Science, Vol. 7, Issue 4 https://doi.org/10.1214/ss/1177011136	journal	November 1992
Estimating Maggot Age from Weight Using Inverse Prediction Wells, Jeffrey D.; LaMotte, Lynn R. Journal of Forensic Sciences, Vol. 40, Issue 4 https://doi.org/10.1520/JFS13830J	journal	July 1995
Stan : A Probabilistic Programming Language Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D. Journal of Statistical Software, Vol. 76, Issue 1 https://doi.org/10.18637/jss.v076.i01	journal	January 2017

Similar Records

Analysis Code - Data Analysis in 'Leveraging Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications' (LMSMIPNFA) v. 1.0

Software · Sun Mar 18 20:00:00 EDT 2018 · OSTI ID:code-17991

Design of experiments and data analysis challenges in calibration for forensics applications

Journal Article · Wed Jul 15 00:00:00 EDT 2015 · Chemometrics and Intelligent Laboratory Systems · OSTI ID:1221551

Validation of Inverse Methods Applied to Forensic Analysis of Spent Fuel

Conference · Thu Dec 31 23:00:00 EST 2009 · OSTI ID:1001291

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
Inverse prediction
Nuclear forensics

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Citation Formats

References (19)

Linked Research (1)

Similar Records

Related Subjects