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ORCIDDaniel W. Apley
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- affiliation: Northwestern University, Evanston, IL, USA
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2020 – today
- 2024
[j37]Özge Sürer
, Daniel W. Apley, Edward C. Malthouse:
Coefficient tree regression: fast, accurate and interpretable predictive modeling. Mach. Learn. 113(7): 4723-4759 (2024)- [i9]Wei Liu, Satyajit Mojumder, Wing Kam Liu, Wei Chen, Daniel W. Apley:
Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores. CoRR abs/2404.15207 (2024) - 2023
- [j36]Shengtong Zhang, Daniel W. Apley:
Interpretable Architecture Neural Networks for Function Visualization. J. Comput. Graph. Stat. 32(4): 1258-1271 (2023) - [j35]Suraj Yerramilli, Akshay Iyer, Wei Chen, Daniel W. Apley:
Fully Bayesian Inference for Latent Variable Gaussian Process Models. SIAM/ASA J. Uncertain. Quantification 11(4): 1357-1381 (2023) - [j34]Kungang Zhang, Anh Tuan Bui, Daniel W. Apley:
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors. Technometrics 65(2): 137-149 (2023) - [i8]Shengtong Zhang, Daniel W. Apley:
Interpretable Architecture Neural Networks for Function Visualization. CoRR abs/2303.03393 (2023) - 2022
- [j33]Anh Tuan Bui, Daniel W. Apley:
Robust monitoring of stochastic textured surfaces. Int. J. Prod. Res. 60(16): 5071-5086 (2022) - [j32]Anh Tuan Bui, Daniel W. Apley:
Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data. Technometrics 64(4): 457-474 (2022) - [i7]Hengrui Zhang, Wei Wayne Chen, Akshay Iyer, Daniel W. Apley, Wei Chen:
Uncertainty-aware Mixed-variable Machine Learning for Materials Design. CoRR abs/2207.04994 (2022) - [i6]Suraj Yerramilli, Akshay Iyer, Wei Chen, Daniel W. Apley:
Fully Bayesian inference for latent variable Gaussian process models. CoRR abs/2211.02218 (2022) - 2021
- [j31]Özge Sürer, Daniel W. Apley, Edward C. Malthouse:
Coefficient tree regression for generalized linear models. Stat. Anal. Data Min. 14(5): 407-429 (2021) - [j30]Ran Yang, David Kent, Daniel W. Apley, Jeremy Staum, David Ruppert:
Bias-corrected Estimation of the Density of a Conditional Expectation in Nested Simulation Problems. ACM Trans. Model. Comput. Simul. 31(4): 22:1-22:36 (2021) - [i5]Liwei Wang, Suraj Yerramilli, Akshay Iyer, Daniel W. Apley, Ping Zhu, Wei Chen:
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors. CoRR abs/2106.15356 (2021) - 2020
- [j29]Yang Yu, Ning Zhang, Daniel W. Apley, Wenxin Jiang:
Including a Nugget Effect in Lifted Brownian Covariance Models. SIAM/ASA J. Uncertain. Quantification 8(4): 1338-1357 (2020) - [j28]Daniel W. Apley:
Technometrics 2019 Editor's Report. Technometrics 62(1): 1-5 (2020) - [j27]Yichi Zhang, Siyu Tao, Wei Chen, Daniel W. Apley:
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors. Technometrics 62(3): 291-302 (2020) - [i4]Kungang Zhang, Anh Tuan Bui, Daniel W. Apley:
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors. CoRR abs/2012.06916 (2020)
2010 – 2019
- 2019
- [j26]Anh Tuan Bui, Daniel W. Apley:
An exploratory analysis approach for understanding variation in stochastic textured surfaces. Comput. Stat. Data Anal. 137: 33-50 (2019) - [j25]Anh Tuan Bui, Joon-Ku Im, Daniel W. Apley, George C. Runger:
Projection-free kernel principal component analysis for denoising. Neurocomputing 357: 163-176 (2019) - [j24]Daniel W. Apley:
Technometrics 2018 Editor's Report. Technometrics 61(1): 2-6 (2019) - [i3]Yichi Zhang, Daniel W. Apley, Wei Chen:
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables. CoRR abs/1910.01688 (2019) - 2018
- [j23]Chiwoo Park, Daniel W. Apley:
Patchwork Kriging for Large-scale Gaussian Process Regression. J. Mach. Learn. Res. 19: 7:1-7:43 (2018) - [j22]Anh Tuan Bui, Daniel W. Apley:
A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces. Technometrics 60(1): 1-13 (2018) - [j21]Phillip Howard, Daniel W. Apley, George Runger:
Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis. IEEE Trans. Neural Networks Learn. Syst. 29(1): 156-166 (2018) - [i2]Yichi Zhang, Siyu Tao, Wei Chen, Daniel W. Apley:
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors. CoRR abs/1806.07504 (2018) - 2017
- [j20]Liwen Ouyang, Daniel W. Apley, Sanjay Mehrotra:
Batch Sample Design from Databases for Logistic Regression. Qual. Reliab. Eng. Int. 33(1): 87-101 (2017) - [j19]Matthew Plumlee, Daniel W. Apley:
Lifted Brownian Kriging Models. Technometrics 59(2): 165-177 (2017) - [j18]Daniel W. Apley:
Technometrics 2017 Editor's Report. Technometrics 59(4): 413-415 (2017) - [i1]Chiwoo Park, Daniel W. Apley:
Patchwork Kriging for Large-scale Gaussian Process Regression. CoRR abs/1701.06655 (2017) - 2016
- [j17]Liwen Ouyang, Daniel W. Apley, Sanjay Mehrotra:
A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records. J. Am. Medical Informatics Assoc. 23(e1): e71-e78 (2016) - [j16]Zhenyu Shi, Daniel W. Apley, George C. Runger:
Discovering the Nature of Variation in Nonlinear Profile Data. Technometrics 58(3): 371-382 (2016) - 2014
- [j15]Anshuman Sahu, Daniel W. Apley, George C. Runger:
Feature selection for noisy variation patterns using kernel principal component analysis. Knowl. Based Syst. 72: 37-47 (2014) - 2012
- [j14]Joon-Ku Im, Daniel W. Apley, C. Qi, X. Shan:
A time-dependent proportional hazards survival model for credit risk analysis. J. Oper. Res. Soc. 63(3): 306-321 (2012) - [j13]Daniel W. Apley:
Posterior Distribution Charts: A Bayesian Approach for Graphically Exploring a Process Mean. Technometrics 54(3): 279-293 (2012) - [j12]Joon-Ku Im, Daniel W. Apley, George C. Runger:
Tangent Hyperplane Kernel Principal Component Analysis for Denoising. IEEE Trans. Neural Networks Learn. Syst. 23(4): 644-656 (2012) - 2011
- [j11]Yunpeng Sun, Daniel W. Apley, Jeremy Staum:
Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation. Oper. Res. 59(4): 998-1007 (2011) - [j10]Hyun Cheol Lee, Daniel W. Apley:
Improved design of robust exponentially weighted moving average control charts for autocorrelated processes. Qual. Reliab. Eng. Int. 27(3): 337-352 (2011) - [c2]Anshuman Sahu, George C. Runger, Daniel W. Apley:
Image denoising with a multi-phase kernel principal component approach and an ensemble version. AIPR 2011: 1-7 - 2010
- [j9]Daniel W. Apley:
Comment. Technometrics 52(3): 277-280 (2010)
2000 – 2009
- 2008
- [j8]Xuemei Shan, Daniel W. Apley:
Blind Identification of Manufacturing Variation Patterns by Combining Source Separation Criteria. Technometrics 50(3): 332-343 (2008) - 2006
- [j7]Chang-Ho Chin, Daniel W. Apley:
Optimal Design of Second-Order Linear Filters for Control Charting. Technometrics 48(3): 337-348 (2006) - 2005
- [j6]Daniel W. Apley, Yu Ding:
A characterization of diagnosability conditions for variance components analysis in assembly operations. IEEE Trans Autom. Sci. Eng. 2(2): 101-110 (2005) - 2004
- [j5]Daniel W. Apley, Jeong-Bae Kim:
Cautious Control of Industrial Process Variability With Uncertain Input and Disturbance Model Parameters. Technometrics 46(2): 188-199 (2004) - 2003
- [j4]Daniel W. Apley, Hyun Cheol Lee:
Design of Exponentially Weighted Moving Average Control Charts for Autocorrelated Processes With Model Uncertainty. Technometrics 45(3): 187-198 (2003) - [j3]Daniel W. Apley, Ho-Young Lee:
Identifying Spatial Variation Patterns in Multivariate Manufacturing Processes - A Blind Separation Approach. Technometrics 45(3): 220-234 (2003) - [c1]Feng Zhang, Daniel W. Apley:
MLPCA Based Logistical Regression Analysis for Pattern Clustering in Manufacturing Processes. IDEAL 2003: 898-902 - 2001
- [j2]Daniel W. Apley, Jianjun Shi:
A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes. Technometrics 43(1): 84-95 (2001)
1990 – 1999
- 1999
- [j1]Daniel W. Apley, Jianjun Shi:
An order downdating algorithm for tracking system order and parameters in recursive least squares identification. IEEE Trans. Signal Process. 47(11): 3134-3137 (1999)
Coauthor Index
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