My research interest lie in design, analysis, and modelling of computer experiments, uncertainty quantification, theory of fractional factorial designs, efficient data collections for real applications in science and engineering, big data and statistical learning.
Submitted:
Chen, G., He, Y., Lin, C.D. and Sun, F. (2024), Grouped Orthogonal Arrays and Their Construction Methods," submitted.
Lin, C.D. and Ranjan, P. (2023). Sequential Designs for Computer Experiments, submitted for book chapter.
3. Pratola, M.T., Lin, C.D. and Craigmile, P.F. (2022), Optimal Design Emulator: A Point Process Approach.
Publications:
28. Cai, X, Xu, L, Lin, C.D., Hong, Y. and Deng, X. (2024), Sequential Design of Computer Experiments with Quantitative and Qualitative Factors in Applications to HPC Performance Optimization, Journal of Quality Technology, 56, 5-19.
27. Li, N., Song, Y., Lin, C.D. and Tu, D. (2023), Bootstrap adjusted predictive classification under generalized linear model, Electronic Journal of Statistics, 17(1), 548-606
26. Li, N., Song, Y., Lin, C.D. and Tu, D. (2023), Bootstrap Adjustment to Minimum p-Value Method for Predictive Classification, Statistica Sinica, 33, 2065-2086.
26. Feng, Y., Lin, C.D., Zhou, Y., and He. Y. (2023), Doubly Coupled Designs for Computer Experiments with both Qualitative and Quantitative Factors, Statistica Sinica. 33, 1923-1942 .
25. He, Y., Lin, C.D. and Sun, F. (2022), A New and Flexible Design Construction for Orthogonal Arrays for Modern Applications, The Annals of Statistics. 50(3), 1473-1489.
24. Qian, X., Mandal, A., Lin, C.D., and Deng, X. (2021), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors, 9(2), Journal on Uncertainty Quantification, 333-353.
23. Yang, F.*, Lin, C.D. and Ranjan, P. (2020), Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator, Journal of Statistical Theory and Practice, 14(9), 1-12.
22. Zhang, R.*, Lin, C.D. and Ranjan, P. (2019), A Sequential Design Approach for Calibrating a Dynamic Population Growth Model, SIAM/ASA Journal on Uncertainty Quantification, 7(4), 1245–1274.
21. He, Y., Lin, C.D. and Sun, F. (2019), Construction of Marginally Coupled Designs by Subspace Theory, Bernoulli, 25, 2163-2182.
20. Zhang, R.*, Lin, C.D. and Ranjan, P. (2018), Local Approximate Gaussian Process Model for Large-scale Dynamic Computer Experiments, Journal of Computational and Graphical Statistics, 27, 798-807.
19. Chen, P.*, Chen, R. and Lin, C.D. (2018), Optimizing Two-level Orthogonal Arrays for Simultaneously Estimating Main Effects and Pre-specified Two-factor Interactions, Computational Statistics and Data Analysis, 118, 84-97.
18. He, Y., Lin, C.D., Sun, F. and Lv, B. (2017), Marginally Coupled Designs For Two-level Qualitative Factors, Journal of Statistical Planning and Inference, 187, 103-108.
17. Deng, X., Lin, C.D., Liu, K.W.* and Rowe, R.K. (2017), Additive Gaussian Process for Computer Models with Qualitative and Quantitative Factors, Technometrics, 59, 3, 283-292.
16. He, Y., Lin, C.D. and Sun, F. (2017), On Construction of Marginally Coupled Designs, Statistica Sinica, 27, 665-683.
15. Lekivetz, R. and Lin, C.D. (2016), Designs of Variable Resolution Robust to Non-negligible Two-factor Interactions, Statistica Sinica, 26, 3, 1269-1278.
14. Lin, C.D. and Kang, L. (2016), A General Construction for Space-filling Latin Hypercubes, Statistica Sinica, 26, 675-690.
13. Deng, X., Hung, Y. and Lin, C.D. (2015), Design for Computer Experiments with Qualitative and Quantitative Factors, Statistica Sinica, 25, 4, 1567-1581.
12. Lin, C.D., Anderson-Cook, C.M., Hamada, M.S., Moore, L.M. and Sitter, R.R. (2015), Using Genetic Algorithms to Design Experiments: A Review, Quality and Reliability Engineering International, 31, 2, 155-167.
11. Lin, C.D. and Morrill*, S. (2014), Design of Variable Resolution for Model Selection. Journal of Statistical Planning and Inference, 155, 127-134.
10. Lin, C.D., Lu, W., Rust, K. and Sitter, R.R. (2013), Replication Variance Estimation in Unequal Probability Sampling Without Replacement: One-Stage and Two-Stage, Canadian Journal of Statistics, 41,4, 696-716.
9. Yang, J., Lin, C.D., Qian, P.Z.G. and Lin, D.K.J. (2013), Construction of Sliced Orthogonal Latin Hypercube Designs, Statistica Sinica, 23, 1117-1130.
8. Lin, C.D. (2012), Design of Variable Resolution, Biometrika, 99, 748-754.
7. Lin, C.D., Sitter, R.R. and Tang, B. (2012), Creating Catalogs of Two-level Non-regular Factorial Designs Based on the Criteria of Generalized Aberration, Journal of Statistical Planning and Inference, 142, 445-456.
6. Linkletter, C., Ranjan, P., Lin, C.D., Bingham, D., Brenneman, W., Lockhart, R. and Loughin, T. (2012), Compliance Testing for Random Effects Models with Joint Acceptance Criterion, Technometrics, 54, 243-255.
5. Lin, C.D., Bingham, D., Sitter, R.R. and Tang, B. (2010), A New and Flexible Method for Constructing Designs for Computer Experiments, Annals of Statistics, 38, 3, 1460-1477.
4. Lin, C.D., Mukerjee, R. and Tang, B. (2009), Construction of Orthogonal and Nearly Orthogonal Latin Hypercubes, Biometrika, 96, 1, 243-247.
3. Lin, C.D., Miller, A. and Sitter, R.R. (2008), Folded Over Non-Orthogonal Designs, Journal of Statistical Planning and Inference, 138, 10, 3107-3124.
2. Lin, C.D. and Sitter, R.R. (2008), An Isomorphism Check for Two-Level Fractional Factorial Designs, Journal of Statistical Planning and Inference, 134, 4, 1085-1101.
1. Lin, C.D., Lu, W. and Sitter, R.R. (2006), Fast Approximately Balanced Bootstrap Without Construction, Statistics and Probability Letters, 76, 1861-1872.
Book Chapters
1. Lin, C.D. and Tang, B. (2015), Latin Hypercubes and Space-lling Designs, Handbook of Design and Analysis of Experiments, Bingham, D., Dean, A., Morris, M., and Stufken, J. ed. 593-626, CRC Press.
2. Deng, X., Hung, Y. and Lin, C.D. (2017), Design and Analysis of Computer Experiments, Handbook of Research on Applied Cybernetics and Systems Science, Saha,S. Mandal, A., Narasimhamurthy, A., Sarasvathi, V. and Sangam, S. ed. 264-279, IGI Global.
3. Chien, P., Deng, X. and Lin, C.D. (2022), The Lasso with Nearly Orthogonal Latin Hypercube Design, Advances and Innovations in Statistics and Data Science, (pp. 295-309). Cham: Springer International Publishing.