Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; Burr, Tom; Hamada, Michael S.

doi:10.1080/00224065.2017.11917992

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 »« less

Authors:

Thomas, Edward V. ^[1]; Lewis, John R. ^[1]; Anderson-Cook, Christine M. ^[2]; Burr, Tom ^[3]; Hamada, Michael S. ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Intl Atomic Energy Agency (IAEA), Vienna (Austria)

Publication Date:: Tue Nov 21 00:00:00 EST 2017

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.

Copy to clipboard


                    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.  https://doi.org/10.1080/00224065.2017.11917992

Copy to clipboard


                    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.  https://doi.org/10.1080/00224065.2017.11917992.  https://www.osti.gov/servlets/purl/1427203.

Copy to clipboard


                    
@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         = {Tue Nov 21 00:00:00 EST 2017},

  month        = {Tue Nov 21 00:00:00 EST 2017}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1080/00224065.2017.11917992

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information
journal, June 1988

Haaland, David M.; Thomas, Edward V.
Analytical Chemistry, Vol. 60, Issue 11
DOI: 10.1021/ac00162a020

Design of experiments and data analysis challenges in calibration for forensics applications
journal, December 2015

Anderson-Cook, Christine; Burr, Tom; Hamada, Michael S.
Chemometrics and Intelligent Laboratory Systems, Vol. 149
DOI: 10.1016/j.chemolab.2015.07.008

Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements
journal, April 2003

Krasnopolsky, Vladimir M.; Schiller, Helmut
Neural Networks, Vol. 16, Issue 3-4
DOI: 10.1016/S0893-6080(03)00027-3

Errors-in-Variables Estimation in Multivariate Calibration
journal, November 1991

Thomas, Edward V.
Technometrics, Vol. 33, Issue 4
DOI: 10.1080/00401706.1991.10484869

Shrinkage Estimators for Covariance Matrices
journal, December 2001

Daniels, Michael J.; Kass, Robert E.
Biometrics, Vol. 57, Issue 4
DOI: 10.1111/j.0006-341X.2001.01173.x

Classical and Inverse Regression Methods of Calibration
journal, August 1967

Krutchkoff, R. G.
Technometrics, Vol. 9, Issue 3
DOI: 10.1080/00401706.1967.10490486

The Inverse Problem in Electrocardiography: A Model Study of the Effects of Geometry and Conductivity Parameters on the Reconstruction of Epicardial Potentials
journal, July 1986

Messinger-Rapport, Barbara J.; Rudy, Yoram
IEEE Transactions on Biomedical Engineering, Vol. BME-33, Issue 7
DOI: 10.1109/TBME.1986.325756

Statistical analysis for nuclear forensics experiments: Statistical Analysis for Nuclear Forensics Experiments
journal, July 2015

Anderson-Cook, Christine M.; Burr, Tom; Hamada, M. S.
Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 8, Issue 5-6
DOI: 10.1002/sam.11278

Response surface design evaluation and comparison
journal, February 2009

Anderson-Cook, Christine M.; Borror, Connie M.; Montgomery, Douglas C.
Journal of Statistical Planning and Inference, Vol. 139, Issue 2
DOI: 10.1016/j.jspi.2008.04.004

Similar Records in DOE PAGES and OSTI.GOV collections:

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

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 dictatesmore »« less
Full Text Available
Dynamic Data-Driven Event Reconstruction for Atmospheric Releases

Technical Report Mirin, A 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
Final Report: Detection and Characterization of Underground Facilities by Stochastic Inversion and Modeling of Data from the New Generation of Synthetic Aperture Satellites

Technical Report Foxall, W ; Cunningham, C ; Mellors, R ; ...

Many clandestine development and production activities can be conducted underground to evade surveillance. The purpose of the study reported here was to develop a technique to detect underground facilities by broad-area search and then to characterize the facilities by inversion of the collected data. This would enable constraints to be placed on the types of activities that would be feasible at each underground site, providing a basis the design of targeted surveillance and analysis for more complete characterization. Excavation of underground cavities causes deformation in the host material and overburden that produces displacements at the ground surface. Such displacements aremore »« less
https://doi.org/10.2172/1036847

Full Text Available
Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

Technical Report Datta-Gupta, Akhil ; Mishra, Srikanta

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from themore »« less
https://doi.org/10.2172/1855768

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

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

Similar Records

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

Abstract

Citation Formats

Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information journal, June 1988

Design of experiments and data analysis challenges in calibration for forensics applications journal, December 2015

Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements journal, April 2003

Errors-in-Variables Estimation in Multivariate Calibration journal, November 1991

Shrinkage Estimators for Covariance Matrices journal, December 2001

Classical and Inverse Regression Methods of Calibration journal, August 1967

The Inverse Problem in Electrocardiography: A Model Study of the Effects of Geometry and Conductivity Parameters on the Reconstruction of Epicardial Potentials journal, July 1986

Statistical analysis for nuclear forensics experiments: Statistical Analysis for Nuclear Forensics Experiments journal, July 2015

Response surface design evaluation and comparison journal, February 2009

Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information
journal, June 1988

Design of experiments and data analysis challenges in calibration for forensics applications
journal, December 2015

Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements
journal, April 2003

Errors-in-Variables Estimation in Multivariate Calibration
journal, November 1991

Shrinkage Estimators for Covariance Matrices
journal, December 2001

Classical and Inverse Regression Methods of Calibration
journal, August 1967

The Inverse Problem in Electrocardiography: A Model Study of the Effects of Geometry and Conductivity Parameters on the Reconstruction of Epicardial Potentials
journal, July 1986

Statistical analysis for nuclear forensics experiments: Statistical Analysis for Nuclear Forensics Experiments
journal, July 2015

Response surface design evaluation and comparison
journal, February 2009