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

Abstract

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictates the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examplesmore » illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Authors:
 [1];  [1]; ORCiD logo [2];  [2]; ORCiD logo [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1375866
Report Number(s):
LA-UR-15-27620
Journal ID: ISSN 0022-4065
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: 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:
97 MATHEMATICS AND COMPUTING; Prediction; Multivariate analysis; Measurement error; Regression analysis; Least squares

Citation Formats

Thomas, Edward V., Lewis, John. R., Anderson-Cook, Christine Michaela, Hamada, Michael Scott, and Burr, Thomas Lee. Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction. United States: N. p., 2017. Web.
Thomas, Edward V., Lewis, John. R., Anderson-Cook, Christine Michaela, Hamada, Michael Scott, & Burr, Thomas Lee. Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction. United States.
Thomas, Edward V., Lewis, John. R., Anderson-Cook, Christine Michaela, Hamada, Michael Scott, and Burr, Thomas Lee. Sat . "Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction". United States. doi:. https://www.osti.gov/servlets/purl/1375866.
@article{osti_1375866,
title = {Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction},
author = {Thomas, Edward V. and Lewis, John. R. and Anderson-Cook, Christine Michaela and Hamada, Michael Scott and Burr, Thomas Lee},
abstractNote = {The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictates the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.},
doi = {},
journal = {Journal of Quality Technology},
number = 3,
volume = 49,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}

Journal Article:
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