D. Apley

Zachary A Abecassis, Lisa M McElroy, Rebeca Khorzad, 2017, American journal of medical quality : the official journal of the American College of Medical Quality.

Characterization and reconstruction of 3D stochastic microstructures via supervised learning

W Chen, R Bostanabad, D W Apley, 2016, Journal of microscopy.

Density Deconvolution With Additive Measurement Errors Using Quadratic Programming

David Ruppert, Ran Yang, Jeremy Staum, 2018, Journal of Computational And Graphical Statistics.

Brownian Integrated Covariance Functions for Gaussian Process Modeling: Sigmoidal Versus Localized Basis Functions

Daniel W. Apley, Ning Zhang, D. Apley, 2016 .

Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables

Wei Chen, Yichi Zhang, Daniel Apley, 2019, Scientific Reports.

A characterization of diagnosability conditions for variance components analysis in assembly operations

Yu Ding, Daniel W. Apley, Yu Ding, 2005, IEEE Transactions on Automation Science and Engineering.

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

Wei Chen, Siyu Tao, Daniel W. Apley, 2018, Technometrics.

Patchwork Kriging for Large-scale Gaussian Process Regression pdf

Chiwoo Park, Daniel W. Apley, Chiwoo Park, 2017, J. Mach. Learn. Res..

Enhanced Collaborative Optimization Using Alternating Direction Method of Multipliers

Guang Yang, Siyu Tao, Wei Chen, 2018 .

A cautious approach to robust design with model parameter uncertainty

Jeongbae Kim, Daniel W. Apley, D. Apley, 2011 .

Daniel W. Apley, Fugee Tsung, Lianjie Shu, 2002 .

Posterior Distribution Charts: A Bayesian Approach for Graphically Exploring a Process Mean

Daniel W. Apley, D. Apley, 2012, Technometrics.

Updating Predictive Models: Calibration, Bias Correction and Identifiability

Daniel W. Apley, Wei Chen, Paul D. Arendt, 2010, DAC 2010.

Image denoising with a multi-phase kernel principal component approach and an ensemble version

Daniel W. Apley, George C. Runger, Anshuman Sahu, 2011, 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

Announcement from the Editor

D. Apley, 2012 .

A Suboptimal N-Step-Ahead Cautious Controller for Adaptive Control Applications

Jianjun Shi, Daniel W. Apley, D. Apley, 1998 .

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

Daniel W. Apley, X. Shan, Joon-Ku Im, 2012, J. Oper. Res. Soc..

Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration

G. Wagner, D. Apley, Tianju Xue, 2023, Computational Mechanics.

Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond

G. Wagner, D. Apley, Hengyang Li, 2023, Computational Mechanics.

Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C-HiDeNN-TD) for high-resolution topology optimization

D. Apley, Wei Chen, Hengyang Li, 2023, Computational Mechanics.

spc4sts: Statistical process control for stochastic textured surfaces in R

Daniel W. Apley, Anh Tuan Bui, D. Apley, 2020, Journal of Quality Technology.

A Structural Equation Modeling Based Approach for Identifying Key Descriptors in Microstructural Materials Design

Wei Chen, D. Apley, Yichi Zhang, 2015, DAC 2015.

Monitoring for changes in the nature of stochastic textured surfaces

Daniel W. Apley, Anh Tuan Bui, D. Apley, 2018, Journal of Quality Technology.

Integrating Bayesian Calibration, Bias Correction, and Machine Learning for the 2014 Sandia Verification and Validation Challenge Problem

Wei Li, Wei Chen, Zhen Jiang, 2016 .

Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors pdf

Akshay Iyer, Daniel Apley, Suraj Yerramilli, 2021, ArXiv.

Tolerance allocation for compliant beam structure assemblies

Daniel W. Apley, Jianjun Shi, B. W. Shiu, 2003 .

Interpretable Architecture Neural Networks for Function Visualization

D. Apley, Shengtong Zhang, 2023, Journal of Computational and Graphical Statistics.

Preimages for variation patterns from kernel PCA and bagging

Daniel W. Apley, George Runger, Amit Shinde, 2014 .

Adaptive CUSUM procedures with EWMA-based shift estimators

Wei Jiang, Daniel W. Apley, Lianjie Shu, 2008 .

Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation

Daniel W. Apley, Jeremy Staum, Yunpeng Sun, 2011, Oper. Res..

Identifying and visualizing nonlinear variation patterns in multivariate manufacturing data

Daniel W. Apley, Feng Zhang, D. Apley, 2007 .

Performance and Robustness of Control Charting Methods for Autocorrelated Data

Daniel W. Apley, Chang-Ho Chin, D. Apley, 2008 .

Diagnostics in Disassembly Unscrewing Operations

Daniel W. Apley, Jianjun Shi, G. Seliger, 1998 .

Coefficient tree regression for generalized linear models

Daniel W. Apley, Edward C. Malthouse, Özge Sürer, 2021, Statistical analysis and data mining.

Discovering interpretable structure in longitudinal predictors via coefficient trees

E. Malthouse, D. Apley, Özge Sürer, 2023, Advances in Data Analysis and Classification.

Coefficient tree regression: fast, accurate and interpretable predictive modeling

E. Malthouse, D. Apley, Özge Sürer, 2021, Mach. Learn..

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning

D. Apley, V. Dravid, Carolin B. Wahl, 2023, Proceedings of the National Academy of Sciences of the United States of America.

Discovering the Nature of Variation in Nonlinear Profile Data

Daniel W. Apley, George C. Runger, Zhenyu Shi, 2016, Technometrics.

A Cautious Approach to Robust Design with Model Uncertainty

Daniel W. Apley, D. Apley, Jeongbae Kim, 2002 .

Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores

D. Apley, S. Mojumder, Wei Liu, 2024, 2404.15207.

Bias-corrected Estimation of the Density of a Conditional Expectation in Nested Simulation Problems

Daniel W. Apley, Jeremy Staum, Ran Yang, 2021, ACM Trans. Model. Comput. Simul..