Research 

Research Interests:

1.          Statistical and Machine Learning

2.          Statistical Modeling

3.          Bayesian Variable (Feature) Selection

4.          Computer Experiment

5.          Optimal Design

Research Grants:

PI:

2022-2024 National Science and Technology Council, Taiwan, “Multiple Response Tree based Gaussian Process in Computer Experiments with Qualitative and Quantitative Factors".

2020-2022 Ministry of Science and Technology, Taiwan, “Treed Type Surrogate Models with More Categories Based on the Levels of the Qualitative Factors".

2019-2020 Ministry of Science and Technology, Taiwan, “The Efficient Variational Bayesian Algorithms for Structure Selection Problems”.

2017-2019 Ministry of Science and Technology, Taiwan, “Graphical Model Learning via a Sequential Procedure”.

2016-2018 Ministry of Science and Technology, Taiwan, “Integrating Sequential Experimental Design and Variable Selection into Active Learning Algorithms.” (MOST Excellent Junior Research Investigator Grant, Ministry of Science and Technology)

2014-2016 Ministry of Science and Technology, Taiwan, “Bayesian Structure Selection for Multivariate Regression Models.”

2012-2014 National Science Council, Taiwan, “Efficient Design Generators via Population-based Optimization Approaches.”

2010-2012 National Science Council, Taiwan, “Bayesian Variable Selection Approach and Its Applications.”

2009-2010 National Science Council, Taiwan, “A Surrogate Assistant Approach for Global Optimization in Computer Experiment.”

2008-2009 National Science Council, Taiwan, “Surrogate Models in Computer Experiments.”

2007-2008 National Science Council, Taiwan, “Independent Component Analysis via Copula Techniques.”

2006-2007 National Science Council, Taiwan, “A Markov Chain Monte-Carlo Type Matching Pursuit Algorithm and Its Applications (2/2).”

2005-2006 National Science Council, Taiwan, “A Markov Chain Monte-Carlo Type Matching Pursuit Algorithm and Its Applications (1/2).”

2004-2005 National Science Council, Taiwan, “Overcomplete Independent Component Analysis for Blind Source Separation and Unsupervised Learning (2/2).”

2003-2004 National Science Council, Taiwan, “Overcomplete Independent Component Analysis for Blind Source Separation and Unsupervised Learning (1/2).”

Co-PI:

2020-2023 Ministry of Science and Technology, Taiwan, "Employing Kriging Surrogate Model on the Development of Biomass Gasification Micro Combined Heat and Power System"

Co-investigator:

2014-2018 NIH R01GM107639, USA, “Designing Efficient Designs under Model Uncertainty for Biological Studies.”

Publications:

A. Refereed Papers:

1. Wei-Ting Lai, Ray-Bing Chen, Shih-Feng Huang* (2024). A Modified VAR-deGARCH Model for Asynchronous Multivariate Financial Time Series via Variational Bayesian Inference, International Journal of Forecasting, Accepted. 

2. Wei-Ann Lin, Ray-Bing Chen and Chih-Li Sung* (2024). Category Tree Gaussian Process for Computer Experiments with Many-Category Qualitative Factors and Application to Cooling System Design, Journal of Quality Technology, Accepted. 

3. Kuo-Jung Lee, Ray-Bing Chen and Keunbaik Lee* (2024). Robust Bayesian Cumulative Probit Linear Mixed Models for Longitudinal Ordinal Data, Computational Statistics, Accepted.  

4. Xiang-Xin Chen, Ray-Bing Chen and Chih-Yung Wu* (2024). Prediction and Optimization of Heat Transfer Performance of Premixed Methane Impinging Flame Jet Using the Kriging Model and Genetic Algorithm. Appl. Sci., 14, 3731.

5. Hung-Kai Pi, Mei-Hui Guo, Ray-Bing Chen and Shih-Feng Huang (2024). ECOPICA: Empirical Copula-Based Independent Component Analysis, Statistics and Computing, 34, 52. 

6. Ping-Yang Chen, Ray-Bing Chen and Weng Kee Wong (2023). Particle Swarm Optimization for Finding Efficient Longitudinal Exact Designs for Nonlinear Models, The New England Journal of Statistics in Data Science, 1(3), 299-313. 

7. Ping-Yang Chen, Ray-Bing Chen*, Yu-Shi Chen and Weng Kee Wong (2023). Numerical Methods for Finding A-optimal Designs Analytically. Econometrics and Statistics, 28, 155-162. 

8. Fan Zhang, Ray-Bing Chen*, Ying Hung and Xinwei Deng (2023). Indicator-based Bayesian Variable Selection for Gaussian Process Models in Computer Experiments, Computational Statistics and Data Analysis, 185, 107757.

9. Yung-Chieh Lin, Chi‑Hsiang Chu, Yen-Ju Chen, Ray-Bing Chen and Chao-Ching Huang (2023). Early-life Slowly Feeding Progression Pattern Is Associated with Longitudinal Head-size Growth Faltering and Neurodevelopmental Impairment Outcomes in Extremely Preterm Infants, Nutrients, 15(5), 1277.

10. Wen-Hao Yu, Yung-Chieh Lin, Chi-Hsiang Chu, Ray-Bing Chen, Jiunn-Liang Wu and Chao-Ching Huang (2023). Risk Patterns Associated with Transient Hearing Impairment and Permanent Hearing Loss in Infants Born Very Preterm: A Retrospective Study, Developmental Medicine & Child Neurology, 65(4), 479–488.

11. Kuo-Jung Lee, Chanmin Kim, Ray-Bing Chen and Keunbaik Lee* (2022). Robust Probit Linear Mixed Models for Longitudinal Binary Data. Biometrical Journal. 64(7), 1307-1324. 

12. Wen-Hao Yu, Chi-Hsiang Chu, Yung-Chieh Lin, Ray-Bing Chen, Osuke Iwata, Chao-Ching Huang (2022). Early-Life Respiratory Trajectories Are Associated with Neurodevelopmental Outcomes in Extremely Preterm Infants: a Retrospective Clustering Analysis Study. Developmental Medicine & Child Neurology.  64(10), 1246-1253. 

13. Ping-Yang Chen, Ray-Bing Chen and Weng Kee Wong* (2022). Particle Swarm Optimization for Searching Efficient Experimental Designs - A Review. WIREs Computational Statistics. 14(5), e1578.

14. Ping-Yang Chen, Ray-Bing Chen*, Jui-Pin Li and William Li (2022). Particle Swarm Exchange Algorithms with Applications in Generating Optimal Model-Discrimination Designs. Quality Engineering, 34(3),  305-321.

15. Yung-Chieh Lin, Chi-Hsiang Chu, Yen-Ju Chen, Ray-Bing Chen and Chao-Ching Huang* (2022). Gestational Age-Related Associations between Early-life Feeding Trajectories and Growth Outcomes at Term Equivalent Age in Very Preterm Infants. Nutrients, 14(5), 1032.   

16. Wei-Ting Lai, Ray-Bing Chen*, Ying Chen and Thorsten Koch (2022). Variational Bayesian Inference for Network Autoregression Models. Computational Statistics and Data Analysis, 169, 107406.

17. Ray-Bing Chen, Chien-Chih Huang and Weichung Wang* (2021). Particle Swarm Stepwise (PaSS) Algorithm for Information Criteria Based Variable Selections. Journal of Statistical Computation and Simulation, 91(11), 2211-2226.

18. Wei-Ting Lai and Ray-Bing Chen* (2021). A Review of Bayesian Group Selection Approaches for Linear Regression Models. WIREs Computational Statistics, 13, e1513. https://doi.org/10.1002/wics.1513

19. Kuo-Jung Lee, Ray-Bing Chen, Min-Sun Kwak and Keunbaik Lee* (2021). Determination of Correlations in Multivariate Longitudinal Data with Modified Cholesky and Hypersphere Decomposition using Bayesian Variable Selection Approach. Statistics in Medicine, 40(4), 978-997.

20. Ray-Bing Chen, Ping-Yang Chen, Cheng-Lin Hsu and Weng Kee Wong* (2020). Hybrid Algorithms for Generating Optimal  Designs for Discriminating Multiple Nonlinear Models under Various Error Distributional Assumptions.  PLoS ONE, 15(10),  e0239864. 

21. Ray-Bing Chen, Yuan Wang and C. F. Jeff Wu* (2020). Finding Optimal Points for Expensive Functions Using Adaptive RBF-Based Surrogate Model Via Uncertainty Quantification. Journal of Global Optimization, 77, 919-948.

22. Liang-Ching Lin, Ray-Bing Chen, Mong-Na Lo Huang and Meihui Guo* (2020). Huber-type Principal Expectile Component Analysis. Computational Statistics and Data Analysis, 151, 106992. 

23. Chi-Hsiang Chu, Mong-Na Lo Huang, Shih-Feng Huang* and Ray-Bing Chen (2019). Bayesian Structure Selection for Vector Autoregression Model. Journal of Forecasting, 38(5), 422-439.

24. Kuo-Jung Lee* and Ray-Bing Chen (2019). Bayesian Variable Selection in a Finite Mixture of Linear Mixed-Effects Models. Journal of Statistical Computation and Simulation, 89(13), 2434-2453. 

25. Ray-Bing Chen, Chi-Hao Li, Ying Hung and Weichung Wang, (2019), Optimal Non-collapsing Space-filling Designs for Bounded Irregular Experimental Regions, Journal of Computational and Graphical Statistics, 28(1), 74-91.

26. Hsiang-Ling Hsu, Yuan-Chin Ivan Chang, Ray-Bing Chen* (2019). Greedy Active Learning Algorithm for Logistic Regression Models. Computational Statistics and Data Analysis, 129, 119-134.

27. Yuan-chin Ivan Chang*, Ray-Bing Chen (2019), Active Learning with Simultaneous Subject and Variable Selections, Neurocomputing, 329, 495-505.

28. Ying Chen, Linlin Niu, Ray-Bing Chen* and Qiang He, (2019). Sparse-Group Independent Component Analysis with Application to Yield Curves Prediction, Computational Statistics and Data Analysis, 133, 76-89.

29. Wei-Ting Lai, Chien-Hsiun Chen, Hsin Hung, Ray-Bing Chen, Sanjay Shete and Chih-Chieh Wu, (2018), Recognizing Spatial and Temporal Clustering Patterns of Dengue Outbreaks in Taiwan, BMC Infectious Diseases, 18, 254.

30. Ping-Yang Chen, Ray-Bing Chen*, C. Devon Lin, (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.

31. Jiahong K. Chen, Ray-Bing Chen*, Akihiro Fujii, Reiji Suda and Weichung Wang, (2018), Surrogate-Assisted Tuning for Computer Experiments with Qualitative and Quantitative Parameters, Statistica Sinica, 28, 761-789.

32. Ray-Bing Chen, Yi-Chi Chen*, Chi-Hsiang Chu, Kuo-Jung Lee, (2017), On the Determinants of the 2008 Financial Crisis: A Bayesian Approach to the Selection of Groups and Variables, Studies in Nonlinear Dynamics & Econometrics, 21(5), 20160107. https://doi.org/10.1515/snde-2016-0107

33. Ping-Yang Chen, Ray-Bing Chen*, Heng-Chin Tung, Weng Kee Wong, (2017), Standardized Maximim D-optimal Designs for Enzyme Kinetic Inhibition Models, Chemometrics and Intelligent Laboratory Systems, 169, 79-86.

34. Sheng-Mao Chang, Jung-Ying Tzeng and Ray-Bing Chen* (2017). Fast Bayesian Variable Screenings for Binary Response Regressions with Small Sample Size. Journal of Statistical Computation and Simulation, 87(14), 2708-2723.

35. Ray-Bing Chen*, Weichung Wang and C. F. Jeff Wu (2017). Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling, Technometrics, 59(2), 139-152.

36. Ray-Bing Chen*, Chi-Hsiang Chu and Ying Nian Wu (2016). Bayesian Sparse Group Selection. Journal of Computational and Graphical Statistics, 25, 665-683.

37. Sheng-Mao Chang*, Ray-Bing Chen and Yunchan Chi (2016). Bayesian Variable Selection for Probit Model with Componentwise Gibbs Sampler. Communications in Statistics - Simulation and Computation, 45, 2752–2766.

38. Frederick K. H. Phoa*, Ray-Bing Chen, Weichung Wang and Weng Kee Wong (2016). Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques. Technometrics, 58(1), 43-49.

39. Kuo-Jung Lee, Ray-Bing Chen* and Ying Nian Wu (2016). Bayesian Variable Selection for Finite Mixture Model of Linear Regressions. Computational Statistics and Data Analysis, 95, 1-16.

40. Kuo-Jung Lee* and Ray-Bing Chen (2015). BSGS: Bayesian Sparse Group Selection, The R Journal, 7, 122-133.

41. Ray-Bing Chen, Shin-Perng Chang, Weichung Wang*, Heng-Chih Tung and Weng Kee Wong (2015). Minimax Optimal Designs via Particle Swarm Optimization Methods. Statistics and Computing, 25, 975-988.

42. Weng Kee Wong, Ray-Bing Chen, Chien-Chih Huang and Weichung Wang*, (2015). A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models, PLoS ONE, 10(6): e0124720. doi:10.1371.

43. Ray-Bing Chen, Mei-Hui Guo*, Wolfgang K. Hardle, and Shih-Feng Huang (2015). COPICA - Independent Component Analysis via Copula Techniques. Statistics and Computing. 25, 273-288.

44. Ray-Bing Chen* and Dennis K. J. Lin, (2015). A Note on Conditionally Optimal Star Points in Central Composite Designs for Response Surface Methodology. Journal of the Chinese Statistical Association, 53, 145-157.

45. Jiaheng Qiu, Ray-Bing Chen, Weichung Wang and Weng Kee Wong* (2014). Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization. Swarm and Evolutionary Computation, 18, 1-10.

46. Ray-Bing Chen, Yaohung M. Tsai and Weichung Wang* (2014). Adaptive Block Size for Dense QR Factorization in Hybrid CPU-GPU Systems via Statistical Modeling. Parallel Computing, 40, 70-85.

47. Ray-Bing Chen, Ying Chen*, Woflgang K. Hardle (2014). TVICA - Time Varying Independent Component Analysis and Its Application to Financial Data. Computational Statistics and Data Analysis, 74, 95-109.

48. Ray-Bing Chen, Yen-Wen Shu, Ying Hung and Weichung Wang* (2014). Discrete Particle Swarm Optimization for Constructing Uniform Design on Irregular Regions. Computational Statistics and Data Analysis, 72, 282-297.

49. Ray-Bing Chen, Ying-Chao Hung, Weichung Wang*, Sung-Wei Yen (2013). Contour Estimation via Two Fidelity Computer Simulators under Limited Resources. Computational Statistics, 28, 1813-1834.

50. Ray-Bing Chen, Dai-Ni Hsieh, Ying Hung, Weichung Wang* (2013). Optimizing Latin Hypercube Designs by Particle Swarm. Statistics and Computing, 23, 663-676.

51. Ray-Bing Chen*, Jian-Zhong Weng and Chi-Hsiang Chu (2013). Screening Procedure for Supersaturated Designs Using a Bayesian Variable Selection Method. Quality and Reliability Engineering International, 29, 89-101.

52. Weichung Wang, Ray-Bing Chen* and Chia-Lung Hsu (2011). Using Adaptive Multi-Accurate Function Evaluations in a Surrogate-Assisted Method for Computer Experiments. Journal of Computational and Applied Mathematics, 235, 3151-3162.

53. Ray-Bing Chen*, Chi-Hsiang Chu, Te-You Lai and Ying Nian Wu (2011). Stochastic Matching Pursuit for Bayesian Variable Selection. Statistics and Computing, 21(2), 247-259.

54. Ray-Bing Chen, Weichung Wang* and C. F. Jeff Wu (2011). Building surrogates with Overcomplete Bases in Computer Experiments with Applications to Bistable Laser Diodes. IIE Transactions, 43, 39-53.

55. Mong-Na Lo Huang, Chuan-Pin Lee*, Ray-Bing Chen, Thomas Klein (2010). Exact D-optimal Designs for a Second-order Response Surface Model on a Circle with Qualitative Factors. Computational Statistics and Data Analysis, 54, 516-530.

56. Ray-Bing Chen, Yu-Jen Tsai and Dennis K. J. Lin* (2008). Conditional Optimal Small Composite Designs. Journal of Statistics and Applications, 6, 29-48.

57. Ray-Bing Chen*, Weng Kee Wong and Kun-Yu Li (2008). Optimal Minimax Designs over a Prespecified Interval in a Heteroscedastic Polynomial Model. Statistics and Probability Letters, 78, 1914-1921.

58. Weichung Wang and Ray-Bing Chen* (2008). Finding Effective Points by Surrogate Models with Overcomplete Bases. Journal of Computational and Applied Mathematics, 217, 110-122.

59. Ray-Bing Chen* and Ying Nian Wu (2007). A Null Space Method for Over-complete Blind Source Separation. Computational Statistics and Data Analysis, 51(12), 5519-5536.

60. Ray-Bing Chen* and Shih-Wei Chiang (2006). Overcomplete Blind Source Separation for Time-series Processes. Journal of the Chinese Statistical Association, 44, 342-363.

61. Mong-Na Lo Huang, Ray-Bing Chen*, Chun-Shi Lin and Weng Kee Wong (2006). Optimal Designs for Parallel Models with Correlated Responses. Statistica Sinica, 16(1), 121-133.

62. Mong-Na Lo Huang, Ray-Bing Chen* and Ying-Ying Chen (2005). c-optimal Designs for Weighted Polynomial Regression Models. Sankhya: The Indian Journal of Statistics, 67(1), 90-105.

63. Ray-Bing Chen and Mong-Na Lo Huang* (2000). Exact D-optimal Designs for Weighted Polynomial Regression Model. Computational Statistics and Data Analysis, 33(2), 137-149.

64. Ray-Bing Chen and Mong-Na Lo Huang* (1994). A Study on Exact D-optimal Design for Polynomial Regression. Journal of the Chinese Statistical Association, 32, 517-540. (in Chinese)

B. Book Chapters:

1. Ray-Bing Chen, Ying Chen and Qian He*, (2017) Penalized Independent Factor, in W. Hardle, C. Y. Chen & L. Overbeck (eds), Applied quantitative finance, 3rd Edition, Springer Science & Business Media. Pages 177-206.

2. Ray-Bing Chen, Ping-Yang Chen, Heng-Chin Tung, and Weng Kee Wong* (2015). Exact D-Optimal Designs for Michaelis-Menten Model with Correlated Observations by Particle Swarm Optimization. Festschrift in Honor of Hans Nyquist on the Occasion of His 65th Birthday (ISBN: 978-91-87355-19-6). Stockholm, Sweden: Department of Statistics, Stockholm University. Page 60-73.

C. Proceedings:

1. Ping-Yang Chen; Chi-Chun Hsia; Yen-Hao Su; Ray-Bing Chen; Sheng-Mao Chang, (2017), Feedback Control for Binary Response, 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taipei, Taiwan, 2017, pp. 21-24. doi:10.1109"TAAI.2017.23.

2. Mu Yang, Ray-Bing Chen, I-Hsin Chung, Weichung Wang (2016). Particle Swarm Stepwise Algorithm (PaSS) on Multicore Hybrid CPU-GPU Clusters. 2016 IEEE International Conference on Computer and Information Technology (CIT 2016), Nadi, Fiji. 265-272.

3. Wan-Ping Chen, Ying Nian Wu and Ray-Bing Chen*, (2014). Bayesian Variable Selection for Multi-responses Linear Regression. In S.-M. Cheng and M.-Y. Day (Eds.): Technologies and Applications of Artificial Intelligence, Lecture Notes in Computer Science, 8916, 74-88.

4. Yaohung M. Tsai*, Ray-Bing Chen, and Weichung Wang (2012). Tuning Block Size for QR Factorization on CPU-GPU Hybrid Systems. Special Session: Auto-Tuning for Multicore and GPU (ATMG) in Conjunction with the IEEE 6th International Symposium on Embedded Multicore SoCs, Aizu-Wakamatsu, Japan.

5. Ray-Bing Chen, Dennis K. J. Lin* and Yu-Jen Tsai (2006). Conditional Optimal Composite Designs. In Proceedings of 2006 International Conference on Design of Experiments and Its Applications, Available on http://stat.nankai.edu.cn/doe.htm

6. Ray-Bing Chen* and Ying Nian Wu (2002). A Null-space Representation for Overcomplete Independent Component Analysis. 2002 Proceedings of American Statistical Association, Statistical Computing Section [CD-ROM]. Alexandria, VA: American Statistical Association.