Comparing multiple statistical methods for inverse prediction in nuclear forensics applications
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.
 Authors:

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 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Publication Date:
 Report Number(s):
 LAUR1725139
Journal ID: ISSN 01697439; TRN: US1800807
 Grant/Contract Number:
 AC5206NA25396
 Type:
 Accepted Manuscript
 Journal Name:
 Chemometrics and Intelligent Laboratory Systems
 Additional Journal Information:
 Journal Volume: 175; Journal ID: ISSN 01697439
 Publisher:
 Elsevier
 Research Org:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org:
 U.S. Department of Homeland Security; USDOE
 Country of Publication:
 United States
 Language:
 English
 Subject:
 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Inverse prediction; Nuclear forensics
 OSTI Identifier:
 1415409
Lewis, John R., Zhang, Adah, and AndersonCook, Christine Michaela. Comparing multiple statistical methods for inverse prediction in nuclear forensics applications. United States: N. p.,
Web. doi:10.1016/j.chemolab.2017.10.010.
Lewis, John R., Zhang, Adah, & AndersonCook, Christine Michaela. Comparing multiple statistical methods for inverse prediction in nuclear forensics applications. United States. doi:10.1016/j.chemolab.2017.10.010.
Lewis, John R., Zhang, Adah, and AndersonCook, Christine Michaela. 2017.
"Comparing multiple statistical methods for inverse prediction in nuclear forensics applications". United States.
doi:10.1016/j.chemolab.2017.10.010. https://www.osti.gov/servlets/purl/1415409.
@article{osti_1415409,
title = {Comparing multiple statistical methods for inverse prediction in nuclear forensics applications},
author = {Lewis, John R. and Zhang, Adah and AndersonCook, Christine Michaela},
abstractNote = {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.},
doi = {10.1016/j.chemolab.2017.10.010},
journal = {Chemometrics and Intelligent Laboratory Systems},
number = ,
volume = 175,
place = {United States},
year = {2017},
month = {10}
}