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Research Output 1994 2019

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Article
2019

A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors

Zhang, Y., Tao, S., Chen, W. & Apley, D., Jan 1 2019, (Accepted/In press) In : Technometrics.

Research output: Contribution to journalArticle

Latent Variables
Process Modeling
Parameterization
Covariance matrix
Gaussian Process
1 Citation (Scopus)

An exploratory analysis approach for understanding variation in stochastic textured surfaces

Bui, A. T. & Apley, D., Sep 1 2019, In : Computational Statistics and Data Analysis. 137, p. 33-50 18 p.

Research output: Contribution to journalArticle

Exploratory Analysis
Dissimilarity
Quality Control
Quality control
Dissimilarity Measure

Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Shi, Z., Apley, D. & Runger, G. C., Jan 1 2019, In : Journal of Quality Technology. 51, 1, p. 3-20 18 p.

Research output: Contribution to journalArticle

Quality control
Surface measurement
Specifications
Wealth
Dimensionality

Input mapping for model calibration with application to wing aerodynamics

Tao, S., Apley, D., Chen, W., Garbo, A., Pate, D. J. & German, B. J., Jan 1 2019, In : AIAA journal. 57, 7, p. 2734-2745 12 p.

Research output: Contribution to journalArticle

Aerodynamics
Calibration
Vortex flow
Geometry

Projection-free kernel principal component analysis for denoising

Bui, A. T., Im, J. K., Apley, D. & Runger, G. C., Sep 10 2019, In : Neurocomputing. 357, p. 163-176 14 p.

Research output: Contribution to journalArticle

Principal Component Analysis
Principal component analysis
Observation
Uncertainty Propagation
Frequency Response Function
Gaussian Model
Gaussian Process
Process Model
2018
5 Citations (Scopus)

A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces

Bui, A. T. & Apley, D., Jan 2 2018, In : Technometrics. 60, 1, p. 1-13 13 p.

Research output: Contribution to journalArticle

Diagnostics
Monitoring
Supervised Learning
Supervised learning
Defects

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis

Howard, P., Apley, D. W. & Runger, G., Jan 1 2018, In : IEEE Transactions on Neural Networks and Learning Systems. 29, 1, p. 156-166 11 p., 7707373.

Research output: Contribution to journalArticle

Principal component analysis
Neural networks
Backpropagation
1 Citation (Scopus)

Enhanced Collaborative Optimization Using Alternating Direction Method of Multipliers

Tao, S., Shintani, K., Yang, G., Meingast, H., Apley, D. & Chen, W., Oct 1 2018, In : Structural and Multidisciplinary Optimization. 58, 4, p. 1571-1588 18 p.

Research output: Contribution to journalArticle

Method of multipliers
Alternating Direction Method
Optimization Methods
Optimization
Multidisciplinary Design Optimization

Identifying nonlinear variation patterns with deep autoencoders

Howard, P., Apley, D. & Runger, G., Dec 2 2018, In : IISE Transactions. 50, 12, p. 1089-1103 15 p.

Research output: Contribution to journalArticle

Quality control
11 Citations (Scopus)

Leveraging the nugget parameter for efficient Gaussian process modeling

Bostanabad, R., Kearney, T., Tao, S., Apley, D. & Chen, W., May 4 2018, In : International Journal for Numerical Methods in Engineering. 114, 5, p. 501-516 16 p.

Research output: Contribution to journalArticle

Process Modeling
Gaussian Process
Hyperparameters
Gaussian Model
Likelihood Function
2 Citations (Scopus)

Monitoring for changes in the nature of stochastic textured surfaces

Bui, A. T. & Apley, D., Jan 1 2018, In : Journal of Quality Technology. 50, 4, p. 363-378 16 p.

Research output: Contribution to journalArticle

Monitoring
Control surfaces
Supervised learning
Learning algorithms
Textiles
2 Citations (Scopus)

Patchwork kriging for large-scale Gaussian process regression

Park, C. & Apley, D., Jul 1 2018, In : Journal of Machine Learning Research. 19, p. 1-43 43 p.

Research output: Contribution to journalArticle

Kriging
Gaussian Process
Regression
Gaussian Model
Pseudo-observations
2017
69 Citations (Scopus)

A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D., Brinson, L. C., Chen, W. & Liu, W. K., Jun 15 2017, In : Computer Methods in Applied Mechanics and Engineering. 320, p. 633-667 35 p.

Research output: Contribution to journalArticle

machine learning
Learning systems
experiment design
plastic properties
finite element method

ANOVA models for Brownian motion

Hazen, G. B., Apley, D. & Parikh, N., Aug 3 2017, In : Communications in Statistics - Theory and Methods. 46, 15, p. 7642-7660 19 p.

Research output: Contribution to journalArticle

Brownian motion
Tables
Tumor Growth
Fixed Effects
Covariance Structure
1 Citation (Scopus)

Batch Sample Design from Databases for Logistic Regression

Ouyang, L., Apley, D. & Mehrotra, S., Feb 1 2017, In : Quality and Reliability Engineering International. 33, 1, p. 87-101 15 p.

Research output: Contribution to journalArticle

Logistics
Design of experiments
Sampling
Entropy
Experiments
1 Citation (Scopus)

Fault Tree Analysis: Assessing the Adequacy of Reporting Efforts to Reduce Postoperative Bloodstream Infection

McElroy, L. M., Khorzad, R., Rowe, T. A., Abecassis, Z. A., Apley, D. W., Barnard, C. & Holl, J. L., Jan 1 2017, In : American Journal of Medical Quality. 32, 1, p. 80-86 7 p.

Research output: Contribution to journalArticle

Centers for Medicare and Medicaid Services (U.S.)
Quality Improvement
Infection
Joints
Publications
3 Citations (Scopus)

Lifted Brownian Kriging Models

Plumlee, M. & Apley, D. W., Apr 3 2017, In : Technometrics. 59, 2, p. 165-177 13 p.

Research output: Contribution to journalArticle

Kriging
Computer simulation
Computer Simulation
Model
Covariance Function
2016
4 Citations (Scopus)
Medical Records
Electronic Health Records
Logistic Models
Databases
Sudden Cardiac Death
14 Citations (Scopus)
Calibration
Experiments
Computer simulation
Covariance matrix
Design of experiments
5 Citations (Scopus)
Covariance Function
Process Modeling
Gaussian Process
Basis Functions
Predictors
15 Citations (Scopus)

Characterization and reconstruction of 3D stochastic microstructures via supervised learning

Bostanabad, R., Chen, W. & Apley, D., Dec 1 2016, In : Journal of Microscopy. 264, 3, p. 282-297 16 p.

Research output: Contribution to journalArticle

Learning
Spatial Analysis
Datasets
Direction compound

Designed sampling from large databases for controlled trials

Ouyang, L., Apley, D. & Mehrotra, S., Dec 1 2016, In : IIE Transactions (Institute of Industrial Engineers). 48, 12, p. 1087-1097 11 p.

Research output: Contribution to journalArticle

Sampling
Design of experiments
Marketing
Sales
Optimal design
5 Citations (Scopus)

Discovering the Nature of Variation in Nonlinear Profile Data

Shi, Z., Apley, D. & Runger, G. C., Jul 2 2016, In : Technometrics. 58, 3, p. 371-382 12 p.

Research output: Contribution to journalArticle

Monitoring
Supervised learning
Animation
Quality control
Websites
3 Citations (Scopus)

Estimating the density of a conditional expectation

Steckley, S. G., Henderson, S. G., Ruppert, D., Yang, R., Apley, D. & Staum, J., Jan 1 2016, In : Electronic Journal of Statistics. 10, 1, p. 736-760 25 p.

Research output: Contribution to journalArticle

Conditional Expectation
Estimator
Kernel Density Estimation
Convergence Results
Sample Size
Learning systems
Calibration
Random processes
Sensitivity analysis
9 Citations (Scopus)

Nonhierarchical multi-model fusion using spatial random processes

Chen, S., Jiang, Z., Yang, S., Apley, D. & Chen, W., May 18 2016, In : International Journal for Numerical Methods in Engineering. 106, 7, p. 503-526 24 p.

Research output: Contribution to journalArticle

Spatial Process
Multi-model
Random process
Random processes
Fidelity
8 Citations (Scopus)

Reduction of Epistemic Model Uncertainty in Simulation-Based Multidisciplinary Design

Jiang, Z., Chen, S., Apley, D. & Chen, W., Aug 1 2016, In : Journal of Mechanical Design, Transactions Of the ASME. 138, 8, 081403.

Research output: Contribution to journalArticle

Uncertainty analysis
Sensitivity analysis
Resource allocation
Decision making
Electronics packaging
59 Citations (Scopus)

Stochastic microstructure characterization and reconstruction via supervised learning

Bostanabad, R., Bui, A. T., Xie, W., Apley, D. & Chen, W., Jan 15 2016, In : Acta Materialia. 103, p. 89-102 14 p.

Research output: Contribution to journalArticle

Supervised learning
Microstructure
Materials science
Pixels
2015
15 Citations (Scopus)
Random processes
Electronics packaging
Uncertainty
Uncertainty analysis
57 Citations (Scopus)

Local Gaussian Process Approximation for Large Computer Experiments

Gramacy, R. B. & Apley, D., Jan 1 2015, In : Journal of Computational and Graphical Statistics. 24, 2, p. 561-578 18 p.

Research output: Contribution to journalArticle

Computer Experiments
Gaussian Process
Approximation
Predictors
Feature Modeling

New Metrics for Validation of Data-Driven Random Process Models in Uncertainty Quantification

Xu, H., Jiang, Z., Apley, D. & Chen, W., Dec 10 2015, In : ASME Journal of Verification, Validation and Uncertainty Quantification. 1, 2, p. 011002-1—011002-14 14 p.

Research output: Contribution to journalArticle

Random processes
Stochastic models
Chaos theory
Materials properties
Uncertainty
4 Citations (Scopus)
Model Calibration
Identifiability
Calibration
Multiple Responses
Uncertainty Quantification
2014
11 Citations (Scopus)

Feature selection for noisy variation patterns using kernel principal component analysis

Sahu, A., Apley, D. & Runger, G. C., Jan 1 2014, In : Knowledge-Based Systems. 72, p. 37-47 11 p.

Research output: Contribution to journalArticle

Principal component analysis
Feature extraction
Kernel
Feature selection
8 Citations (Scopus)

Fractional Brownian fields for response surface metamodeling

Zhang, N. & Apley, D., Jan 1 2014, In : Journal of Quality Technology. 46, 4, p. 285-301 17 p.

Research output: Contribution to journalArticle

Computer simulation
Covariance matrix
Data structures
Statistics
Random field
8 Citations (Scopus)

Preimages for variation patterns from kernel PCA and bagging

Shinde, A., Sahu, A., Apley, D. & Runger, G., May 1 2014, In : IIE Transactions (Institute of Industrial Engineers). 46, 5, p. 429-456 28 p.

Research output: Contribution to journalArticle

Principal component analysis
Inspection
Availability
Industry
2013
19 Citations (Scopus)
Sampling
Interpolation
Uncertainty
Simulators
Computer simulation
2012
15 Citations (Scopus)

A time-dependent proportional hazards survival model for credit risk analysis

Im, J. K., Apley, D., Qi, C. & Shan, X., Mar 1 2012, In : Journal of the Operational Research Society. 63, 3, p. 306-321 16 p.

Research output: Contribution to journalArticle

Risk analysis
Hazards
Maximum likelihood
Logistics
Industry
57 Citations (Scopus)

Improving identifiability in model calibration using multiple responses

Arendt, P. D., Apley, D., Chen, W., Lamb, D. & Gorsich, D., Oct 8 2012, In : Journal of Mechanical Design, Transactions Of the ASME. 134, 10, 100909.

Research output: Contribution to journalArticle

Calibration
Physics
Uncertainty
Experiments
10 Citations (Scopus)

Posterior distribution charts: A Bayesian approach for graphically exploring a process mean

Apley, D., Aug 1 2012, In : Technometrics. 54, 3, p. 279-293 15 p.

Research output: Contribution to journalArticle

Process Mean
Random errors
Posterior distribution
Chart
Bayesian Approach
132 Citations (Scopus)
Calibration
Uncertainty
Physics
8 Citations (Scopus)

Tangent Hyperplane Kernel Principal Component Analysis for Denoising

Im, J. K., Apley, D. W. & Runger, G., Apr 2012, In : IEEE Transactions on Neural Networks. 23, p. 644-656

Research output: Contribution to journalArticle

Principal component analysis
Tuning
2011
22 Citations (Scopus)

A cautious approach to robust design with model parameter uncertainty

Apley, D. & Kim, J., Jul 1 2011, In : IIE Transactions (Institute of Industrial Engineers). 43, 7, p. 471-482 12 p.

Research output: Contribution to journalArticle

Model structures
Mean square error
Design of experiments
Feedback control
Uncertainty
22 Citations (Scopus)

Efficient nested simulation for estimating the variance of a conditional expectation

Sun, Y., Apley, D. & Staum, J. C., Jul 1 2011, In : Operations Research. 59, 4, p. 998-1007 10 p.

Research output: Contribution to journalArticle

Decision theory
Analysis of variance (ANOVA)
Risk management
Error analysis
Experiments
13 Citations (Scopus)
Covariance matrix
Time series
Statistics
Control charts
Exponentially weighted moving average
2010

Discussion of nonparametric profile monitoring by mixed effects modeling

Apley, D. W., Aug 2010, In : Technometrics. 52, p. 277-280

Research output: Contribution to journalArticle

9 Citations (Scopus)

Simultaneous identification of premodeled and unmodeled variation patterns

Apley, D. & Lee, H. Y., Jan 1 2010, In : Journal of Quality Technology. 42, 1, p. 36-51 16 p.

Research output: Contribution to journalArticle

Paradigm
Modeling
Manufacturing
1 Citation (Scopus)

The effects of model parameter deviations on the variance of a linearly filtered time series

Apley, D. & Lee, H. C., Aug 1 2010, In : Naval Research Logistics. 57, 5, p. 460-471 12 p.

Research output: Contribution to journalArticle

Linear Filtering
Time series
Deviation
Autoregressive Moving Average
Linearly
2009
115 Citations (Scopus)

A better understanding of model updating strategies in validating engineering models

Xiong, Y., Chen, W., Tsui, K. L. & Apley, D., Mar 15 2009, In : Computer Methods in Applied Mechanics and Engineering. 198, 15-16, p. 1327-1337 11 p.

Research output: Contribution to journalArticle

engineering
Maximum likelihood estimation
Calibration
formulations
Heat problems
2008
73 Citations (Scopus)

Adaptive CUSUM procedures with EWMA-based shift estimators

Jiang, W., Shu, L. & Apley, D., Aug 18 2008, In : IIE Transactions (Institute of Industrial Engineers). 40, 10, p. 992-1003 12 p.

Research output: Contribution to journalArticle

Testing
Control charts