An order downdating algorithm for tracking system order and parameters in recursive least squares identification. - Daniel W. Apley; Jianjun Shi 001 (IEEE Trans. Signal Process., 1999)

A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes. - Daniel W. Apley; Jianjun Shi 001 (Technometrics, 2001)

MLPCA Based Logistical Regression Analysis for Pattern Clustering in Manufacturing Processes. - Feng Zhang; Daniel W. Apley (IDEAL, 2003)

Design of Exponentially Weighted Moving Average Control Charts for Autocorrelated Processes With Model Uncertainty. - Daniel W. Apley; Hyun Cheol Lee (Technometrics, 2003)

Identifying Spatial Variation Patterns in Multivariate Manufacturing Processes - A Blind Separation Approach. - Daniel W. Apley; Ho-Young Lee (Technometrics, 2003)

Cautious Control of Industrial Process Variability With Uncertain Input and Disturbance Model Parameters. - Daniel W. Apley; Jeong-Bae Kim (Technometrics, 2004)

A characterization of diagnosability conditions for variance components analysis in assembly operations. - Daniel W. Apley; Yu Ding 002 (IEEE Trans Autom. Sci. Eng., 2005)

Optimal Design of Second-Order Linear Filters for Control Charting. - Chang-Ho Chin; Daniel W. Apley (Technometrics, 2006)

Blind Identification of Manufacturing Variation Patterns by Combining Source Separation Criteria. - Xuemei Shan; Daniel W. Apley (Technometrics, 2008)

Comment. - Daniel W. Apley (Technometrics, 2010)

Image denoising with a multi-phase kernel principal component approach and an ensemble version. - Anshuman Sahu; George C. Runger; Daniel W. Apley (AIPR, 2011)

Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation. - Yunpeng Sun; Daniel W. Apley; Jeremy Staum (Oper. Res., 2011)

Improved design of robust exponentially weighted moving average control charts for autocorrelated processes. - Hyun Cheol Lee; Daniel W. Apley (Qual. Reliab. Eng. Int., 2011)

A time-dependent proportional hazards survival model for credit risk analysis. - Joon-Ku Im; Daniel W. Apley; C. Qi; X. Shan (J. Oper. Res. Soc., 2012)

Posterior Distribution Charts: A Bayesian Approach for Graphically Exploring a Process Mean. - Daniel W. Apley (Technometrics, 2012)

Tangent Hyperplane Kernel Principal Component Analysis for Denoising. - Joon-Ku Im; Daniel W. Apley; George C. Runger (IEEE Trans. Neural Networks Learn. Syst., 2012)

Feature selection for noisy variation patterns using kernel principal component analysis. - Anshuman Sahu; Daniel W. Apley; George C. Runger (Knowl. Based Syst., 2014)

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records. - Liwen Ouyang; Daniel W. Apley; Sanjay Mehrotra (J. Am. Medical Informatics Assoc., 2016)

Discovering the Nature of Variation in Nonlinear Profile Data. - Zhenyu Shi; Daniel W. Apley; George C. Runger (Technometrics, 2016)

Batch Sample Design from Databases for Logistic Regression. - Liwen Ouyang; Daniel W. Apley; Sanjay Mehrotra (Qual. Reliab. Eng. Int., 2017)

Technometrics 2017 Editor's Report. - Daniel W. Apley (Technometrics, 2017)

Lifted Brownian Kriging Models. - Matthew Plumlee; Daniel W. Apley (Technometrics, 2017)

Patchwork Kriging for Large-scale Gaussian Process Regression. - Chiwoo Park; Daniel W. Apley (J. Mach. Learn. Res., 2018)

A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces. - Anh Tuan Bui; Daniel W. Apley (Technometrics, 2018)

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis. - Phillip Howard; Daniel W. Apley; George Runger (IEEE Trans. Neural Networks Learn. Syst., 2018)

Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables. - Yichi Zhang; Daniel W. Apley; Wei Chen (CoRR, 2019)

An exploratory analysis approach for understanding variation in stochastic textured surfaces. - Anh Tuan Bui; Daniel W. Apley (Comput. Stat. Data Anal., 2019)

Projection-free kernel principal component analysis for denoising. - Anh Tuan Bui; Joon-Ku Im; Daniel W. Apley; George C. Runger (Neurocomputing, 2019)

Technometrics 2018 Editor's Report. - Daniel W. Apley (Technometrics, 2019)

Including a Nugget Effect in Lifted Brownian Covariance Models. - Yang Yu 078; Ning Zhang; Daniel W. Apley; Wenxin Jiang 003 (SIAM/ASA J. Uncertain. Quantification, 2020)

Technometrics 2019 Editor's Report. - Daniel W. Apley (Technometrics, 2020)

A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors. - Yichi Zhang; Siyu Tao 003; Wei Chen 041; Daniel W. Apley (Technometrics, 2020)

Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors. - Liwei Wang; Suraj Yerramilli; Akshay Iyer; Daniel W. Apley; Ping Zhu 006; Wei Chen 041 (CoRR, 2021)

Coefficient tree regression for generalized linear models. - Özge Sürer; Daniel W. Apley; Edward C. Malthouse (Stat. Anal. Data Min., 2021)

Bias-corrected Estimation of the Density of a Conditional Expectation in Nested Simulation Problems. - Ran Yang; David Kent 002; Daniel W. Apley; Jeremy Staum; David Ruppert (ACM Trans. Model. Comput. Simul., 2021)

Uncertainty-aware Mixed-variable Machine Learning for Materials Design. - Hengrui Zhang; Wei Wayne Chen; Akshay Iyer; Daniel W. Apley; Wei Chen 041 (CoRR, 2022)

Robust monitoring of stochastic textured surfaces. - Anh Tuan Bui; Daniel W. Apley (Int. J. Prod. Res., 2022)

Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data. - Anh Tuan Bui; Daniel W. Apley (Technometrics, 2022)

Interpretable Architecture Neural Networks for Function Visualization. - Shengtong Zhang; Daniel W. Apley (J. Comput. Graph. Stat., 2023)

Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors. - Kungang Zhang; Anh Tuan Bui; Daniel W. Apley (Technometrics, 2023)

Fully Bayesian Inference for Latent Variable Gaussian Process Models. - Suraj Yerramilli; Akshay Iyer; Wei Chen 041; Daniel W. Apley (SIAM/ASA J. Uncertain. Quantification, 2023)

Plausible Inference with a Plausible Lipschitz Constant. - Gregory Keslin; Daniel W. Apley; Barry L. Nelson (WSC, 2024)

Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores. - Wei Liu; Satyajit Mojumder; Wing Kam Liu; Wei Chen; Daniel W. Apley (CoRR, 2024)

Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data. - Hengrui Zhang; Alexandru B. Georgescu; Suraj Yerramilli; Christopher Karpovich; Daniel W. Apley; Elsa A. Olivetti; James M. Rondinelli; Wei Chen 041 (CoRR, 2024)

Coefficient tree regression: fast, accurate and interpretable predictive modeling. - Özge Sürer; Daniel W. Apley; Edward C. Malthouse (Mach. Learn., 2024)

Discovering interpretable structure in longitudinal predictors via coefficient trees. - Özge Sürer; Daniel W. Apley; Edward C. Malthouse (Adv. Data Anal. Classif., 2024)

A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images. - Kungang Zhang; Daniel W. Apley; Wei Chen 041; Wing K. Liu; L. Catherine Brinson (CoRR, 2025)