Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

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

Full Record
Other Related Research

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 »« less

Authors:

Thomas, Edward V. ^[1]; Lewis, John. R. ^[1];

^[2]; Hamada, Michael Scott ^[2];

^[2]

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

Publication Date:: Sat Jul 01 00:00:00 EDT 2017

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.

Copy to clipboard


                    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.

Copy to clipboard


                    Thomas, Edward V., Lewis, John. R., Anderson-Cook, Christine Michaela, Hamada, Michael Scott, and Burr, Thomas Lee. 2017.  
        "Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction".  United States.  https://www.osti.gov/servlets/purl/1375866.

Copy to clipboard


                    
@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          = {},

  url          = {https://www.osti.gov/biblio/1375866},
  journal      = {Journal of Quality Technology},

  issn         = {0022-4065},

  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}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

The DOI is not currently available

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

Journal Article Thomas, Edward; Lewis, John; Anderson-Cook, Christine; ... - Journal of Quality Technology

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 factorsmore »« less
https://doi.org/10.1080/00224065.2017.11917992

Full Text Available
Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Journal Article Lewis, John; Zhang, Adah; Anderson-Cook, Christine - Chemometrics and Intelligent Laboratory Systems

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 inversemore »« less
Cited by 8
https://doi.org/10.1016/j.chemolab.2017.10.010

Full Text Available
Event Reconstruction for Atmospheric Releases Employing Urban Puff Model UDM with Stochastic Inversion Methodology

Conference Neuman, S; Glascoe, L; Kosovic, B; ...

The rapid identification of contaminant plume sources and their characteristics in urban environments can greatly enhance emergency response efforts. Source identification based on downwind concentration measurements is complicated by the presence of building obstacles that can cause flow diversion and entrainment. While high-resolution computational fluid dynamics (CFD) simulations are available for predicting plume evolution in complex urban geometries, such simulations require large computational effort. We make use of an urban puff model, the Defence Science Technology Laboratory's (Dstl) Urban Dispersion Model (UDM), which employs empirically based puff splitting techniques. UDM enables rapid urban dispersion simulations by combining traditional Gaussian puffmore »« less
Full Text Available
Dynamic Data-Driven Event Reconstruction for Atmospheric Releases

Technical Report Mirin, A; Kosovic, B

Accidental or terrorist releases of hazardous materials into the atmosphere can impact large populations and cause significant loss of life or property damage. Plume predictions have been shown to be extremely valuable in guiding an effective and timely response. The two greatest sources of uncertainty in the prediction of the consequences of hazardous atmospheric releases result from poorly characterized source terms and lack of knowledge about the state of the atmosphere as reflected in the available meteorological data. We have developed a new event reconstruction methodology that provides probabilistic source term estimates from field measurement data for both accidental andmore »« less
https://doi.org/10.2172/1046103

Full Text Available
Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk

Journal Article Lee, Sangkyu; Ybarra, Norma; Jeyaseelan, Krishinima; ... - Medical Physics

Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cellmore »« less
https://doi.org/10.1118/1.4915284

Similar Records