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Title: Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

Abstract

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factors and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one anothermore » over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Authors:
 [1];  [1];  [2];  [3];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Intl Atomic Energy Agency (IAEA), Vienna (Austria)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1427203
Report Number(s):
[SAND-2015-6485J]
[Journal ID: ISSN 0022-4065; 598743]
Grant/Contract Number:  
[AC04-94AL85000]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Quality Technology
Additional Journal Information:
[ Journal Volume: 49; Journal Issue: 3]; Journal ID: ISSN 0022-4065
Publisher:
American Society for Quality (ASQ)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Thomas, Edward V., Lewis, John R., Anderson-Cook, Christine M., Burr, Tom, and Hamada, Michael S. Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction. United States: N. p., 2017. Web. doi:10.1080/00224065.2017.11917992.
Thomas, Edward V., Lewis, John R., Anderson-Cook, Christine M., Burr, Tom, & Hamada, Michael S. Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction. United States. doi:10.1080/00224065.2017.11917992.
Thomas, Edward V., Lewis, John R., Anderson-Cook, Christine M., Burr, Tom, and Hamada, Michael S. Tue . "Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction". United States. doi:10.1080/00224065.2017.11917992. https://www.osti.gov/servlets/purl/1427203.
@article{osti_1427203,
title = {Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction},
author = {Thomas, Edward V. and Lewis, John R. and Anderson-Cook, Christine M. and Burr, Tom and Hamada, Michael S.},
abstractNote = {nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factors and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.},
doi = {10.1080/00224065.2017.11917992},
journal = {Journal of Quality Technology},
number = [3],
volume = [49],
place = {United States},
year = {2017},
month = {11}
}

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