陳瑞彬(Ray-Bing Chen)教授基本資料

按下開啟原圖
姓名:
陳瑞彬(Ray-Bing Chen)
職稱:
教授兼系主任
辦公室:
管理學院統計系館4樓62441室
聯絡電話:
(06)2757575~53645
電子郵件:
個人網頁:
Office Hours:  
    ☉主要學歷(Educational Background):
      美國加州大學洛杉磯分校(UCLA)統計學博士

    ☉相關經歷(Main Experiences):
      成功大學統計系教授(2013.08-目前)
      成功大學統計系副教授(2011.02-2013.7)
      高雄大學統計所所長(2008.08-2011.01)
      高雄大學統計所副教授(2008.02-2011.01)
      高雄大學統計所助理教授(2003.08-2008.01)


    ☉專長領域(Specialties):
      統計建模,
      統計計算,
      實驗設計,
      獨立因子分析

    ☉研究方向(Research Field):
      1. 電腦實驗之模型建立及相關實驗設計 (Statistical Modeling in Computer Experiments and Related Experimental Desgins)
      2. n小p大之變數選取方法(Small n and Large p Variable Selection Approach) 
      3. 獨立因子分析之相關演算法 (Independent Component Analysis)
      4. 最適設計之相關研究 (Optimal Designs)

學年度
學期
開課班級
必/選修
學分
科目名稱
授課老師
下載
104
碩博班
選修
3
實驗設計理論與應用
陳瑞彬
 
104
學士三
選修
3
機器學習
陳瑞彬
 
104
碩博班
選修
3
統計專題研究:最適設計專題(一)
陳瑞彬
 
103
學士一
必修
3
微積分(二)
陳瑞彬
 
103
碩博班
選修
3
統計計算與模擬
陳瑞彬
 
103
學士一
必修
3
微積分(一)
陳瑞彬
103
學士三
必修
3
實驗設計
陳瑞彬
102
博士班
必修
3
高等統計推論二
陳瑞彬
102
學士一
必修
3
微積分二
陳瑞彬
102
博士班
必修
3
高等統計推論一
陳瑞彬
102
學士一
必修
3
微積分(一)
陳瑞彬
101
學士一
必修
3
微積分二
陳瑞彬
101
碩博班
選修
3
統計計算與模擬
陳瑞彬
101
學士一
必修
3
微積分(一)
陳瑞彬
101
碩博班
選修
3
實驗設計理論與應用
陳瑞彬
100
學士三
選修
3
統計模擬
陳瑞彬
100
碩博班
選修
3
大樣本方法的理論與應用
陳瑞彬
 
100
學士三
必修
3
實驗設計
陳瑞彬
100
學士四
選修
3
工業統計
陳瑞彬
99
碩博班
選修
3
統計計算與模擬
陳瑞彬
99
博士班
必修
3
高等機率論
陳瑞彬


期刊論文
  1.  Yuan-chin Ivan Chang*; Ray-Bing Chen2019, Active Learning with Simultaneous Subject and Variable Selections, Neurocomputing, 329, 495-505. [SCI]
  2.  Ying Chen, Linlin Niu, Ray-Bing Chen* and Qiang He2019, Sparse-Group Independent Component Analysis with Application to Yield Curves Prediction, Computational Statistics and Data Analysis, 133, 76-89. [SCI]
  3.  Hsiang-Ling Hsu, Yuan-Chin Ivan Chang and Ray-Bing Chen*2019, Greedy Active Learning Algorithm for Logistic Regression Models, Computational Statistics and Data Analysis, 129, 119-134. [SCI]
  4.  Ray-Bing Chen, Chi-Hao Li, Ying Hung and Weichung Wang2019, Optimal Non-collapsing Space-Filling Designs for Bounded Irregular Experimental Regions, Journal of Computational and Graphical Statistics, 28(1), 74-91. [SCI]
  5.  Chi-Hsiang Chu, Mong-Na Lo Huang, Shih-Feng Huang and Ray-Bing Chen2019, Bayesian Structure Selection for Vector Autoregression Model, Journal of Forecasting, 38(5), 422-439. [SSCI]
  6.  Kuo-Jung Lee and Ray-Bing Chen2019, Bayesian Variable Selection in a Finite Mixture of Linear Mixed-Effects Models, Journal of Statistical Computation and Simulation, 89(13), 2434–2453. [SCI]
  7.  Jiahong K. Chen, Ray-Bing Chen*, Akihiro Fujii, Reiji Suda and Weichung Wang2018, Surrogate-Assisted Tuning for Computer Experiments with Qualitative and Quantitative Parameters, Statistica Sinica, 28, 761-789. [SCI]
  8.  Wei-Ting Lai; Chien-Hsiun Chen; Hsin Hung; Ray-Bing Chen; Sanjay Shete; Chih-Chieh Wu2018, Recognizing Spatial and Temporal Clustering Patterns of Dengue Outbreaks in Taiwan, BMC Infectious Diseases, 18:254. [SCI]
  9.  Ping-Yang Chen, Ray-Bing Chen*, C. Devon Lin2018, Optimizing Two-level Orthogonal Arrays for Simultaneously Estimating Main Effects and Pre-specified Two-factor Interactions, Computational Statistics and Data Analysis, 118, 84-97. [SCI]
  10.  Ray-Bing Chen*, Weichung Wang and C. F. Jeff Wu2017,  Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling, Technometrics, 59(2), 139-152. [SCI]
  11.  Ray-Bing Chen, Yi-Chi Chen*, Chi-Hsiang Chu, Kuo-Jung Lee2017, 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), https:""doi.org"10.1515"snde-2016-0107. [SSCI]
  12.  Ping-Yang Chen, Ray-Bing Chen*, Heng-Chin Tung, Weng Kee Wong2017, Standardized Maximim D-optimal Designs for Enzyme Kinetic Inhibition Models, Chemometrics and Intelligent Laboratory Systems, 169, 79-86. [SCI]
  13.  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. [SCI]
  14.  Sheng-Mao Chang*, Ray-Bing Chen, Yunchan Chi2016, Bayesian Variable Selections for Probit Model with Componentwise Gibbs Samplers, Communications in Statistics - Simulation and Computation, 45, 2752–2766. [SCI]
  15.  Kuo-Jung Lee, Ray-Bing Chen* and Ying Nian Wu2016, Bayesian Variable Selection for Finite Mixture Model of Linear Regressions, Computational Statistics and Data Analysis, 95, 1-16. [SCI]
  16.  Ray-Bing Chen*,Chi-Hsiang Chu, Shinsheng Yuan and Ying Nian Wu 2016, Bayesian Sparse Group Selection. Journal of Computational and Graphical Statistics, 25, 665-683. [SCI]
  17.  Frederick K. H. Phoa*, Ray-Bing Chen, Weichung Wang and Weng Kee Wong2016, Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques, Technometrics, 58(1), 43-49. [SCI]
  18.  Ray-Bing Chen, Mei-Hui Guo*, Wolfgang K. Hardle, and Shih-Feng Huang2015, COPICA - Independent Component Analysis via Copula Techniques. Statistics and Computing. 25, 273-288. [SCI]
  19.  Ray-Bing Chen, Shin-Perng Chang, Weichung Wang*, Heng-Chih Tung and Weng Kee Wong2015, Minimax Optimal Designs via Particle Swarm Optimization Methods. Statistics and Computing, 25, 975-988. [SCI]
  20.  Ray-Bing Chen* and Dennis K. J. Lin2015, A Note on Conditionally Optimal Star Points in Central Composite Designs for Response Surface Methodology, Journal of the Chinese Statistical Association, 53, 145-157. [JEL]
  21.  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" journal.pone.0124720 [SCI]
  22.  Kuo-Jung Lee* and Ray-Bing Chen2015, BSGS: Bayesian Sparse Group Selection, The R Journal, 7(2), 122-133. [SCI]
  23.  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.
  24.  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. [SCI]
  25.  Ray-Bing Chen, Ying Chen*, Woflgang K. Hardle2014, TVICA - Time Varying Independent Component Analysis and Its Application to Financial Data. Computational Statistics and Data Analysis, 74, 95-109. [SCI]
  26.  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. [SCI]
  27.  Ray-Bing Chen*, Jian-Zhong Weng and Chi-Hsiang Chu2013, Screening Procedure for Supersaturated Designs Using a Bayesian Variable Selection Method. Quality and Reliability Engineering International, 29, 89-101. [SCI]
  28.  Ray-Bing Chen, Dai-Ni Haieh, Ying Hung and Weichung Wang*2013, Optimizing Latin Hypercube Designs by Particle Swarm. Statistics and Computing, 23, 663-676. [SCI]
  29.  Ray-Bing Chen, Ying-Chao Hung, Weichung Wang*, Sung-Wei Yen2013, Contour Estimation via Two Fidelity Computer Simulators under Limited Resources. Computational Statistics, 28, 1813-1834. [SCI]
  30.  Ray-Bing Chen, Weichung Wang* and C. F. Jeff Wu2011, Building Surrogates with Overcomplete Bases in Computer Experiments with Applications to Bistable Laser Diodes. IIE Transactions, 43, 39-53. [SCI]
  31.  Weichung Wang, Ray-Bing Chen* and Chia-Lung Hsu2011, Using Adaptive Multi-Accurate Function Evaluations in a Surrogate-Assisted Method for Computer Experiments, Journal of Computational and Applied Mathematics, 235, 3151-3162. [SCI]
  32.  Ray-Bing Chen*, Chi-Hsiang Chu, Te-You Lai and Ying Nian Wu2011, Stochastic Matching Pursuit for Bayesian Variable Selection. Statistics and Computing, 21(2), 247-259. [SCI]
  33.  Mong-Na Lo Huang, Chuan-Pin Lee*, Ray-Bing Chen, Thomas Klein2010, 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. [SCI]
  34.  Ray-Bing Chen, Yu-Jen Tsai and Dennis K. J. Lin*2008, Conditional Optimal Small Composite Designs. Journal of Statistics and Applications, 6, 35-56.
  35.  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. [SCI]
  36.  Ray-Bing Chen*, Weng Kee Wong and Kun-Yu Li2008, Optimal Minimax Designs over a Prespecified Interval in a Heteroscedastic Polynomial Model. Statistics and Probability Letters, 78, 1914-1921. [SCI]
  37.  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. [SCI]
  38.  Mong-Na Lo Huang, Ray-Bing Chen*, Chun-Shi Lin and Weng Kee Wong2006, Optimal Designs for Parallel Models with Correlated Responses. Statistica Sinica, 16(1), 121-133. [SCI]
  39.  Ray-Bing Chen* and Shih-Wei Chiang2006, Overcomplete Blind Source Separation for Time-series Processes. Journal of the Chinese Statistical Association, 44, 342-363.
  40.  Mong-Na Lo Huang, Ray-Bing Chen* and Ying-Ying Chen2005, c-optimal Designs for Weighted Polynomial Regression Models. Sankhya: The Indian Journal of Statistics, 67(1), 90-105.
  41.  Ray-Bing Chen and Mong-Na Lo Huang* 2000, Exact D-optimal Designs for Weight Polynomial Regression Model. Computational Statistics and Data Analysis, 33(2), 137-149.
  42.  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)

會議論文
  1.  Ping-Yang Chen; Chi-Chun Hsia; Yen-Hao Su; Ray-Bing Chen; Sheng-Mao Chang2017, 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 Wang2016, 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 Chen2014, 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 Tsai2006, Conditional Optimal Composite Designs. In Proceedings of 2006 International Conference on Design of Experiments and Its Applications.
  6.  Ray-Bing Chen and Ying Nian Wu2002, A Null-space Representation for Overcomplete Independent Component Analysis. 2002 Proceedings of American Statisitcal Association, Statistical Computing Section [CD-ROM]. Alexandria, VA: American Statistical Association.


其他著作
  1.  Ray-Bing Chen, Ying Chen and Qian He2017, Penalized Independent Factor, in W. Hardle, C. Y. Chen & L. Overbeck (eds), Applied Quantitative Finance, 3 edition, Pages 177-206, Springer Science & Business Media.
  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, In E. Fackle-Fornius (Ed.) Festschrift in Honor of Hans Nyquist on the Occasion of His 65th Birthday, pp. 60-73, Department of Statistics, Stockholm University: Stockholm, Sweden.



畢業年份
班別
作者
論文題目
指導教授
資料下載
2015
碩士班
吳柏言
針對兩類及多類分類問題之相關學習策略 陳瑞彬
2015
碩士班
李瑞彬
利用離散型粒子群最佳演算法尋找最佳模型分辨設計 陳瑞彬
2015
碩士班
陳昱熙
在廣義線性模型下之非對稱A-最適設計 陳瑞彬
2014
碩士班
陳慶全
子空間投影之密度函數比估計在二元分類問題之應用 陳瑞彬
2014
碩士班
陳新杰
在分支因子和巢型因子的高斯過程模型下研究次序代基因組裝參數調整 陳瑞彬
    獎勵或榮譽(Honors):
      成功大學管理學院102學年度研究優良教師
      成功大學102學年度教學優良教師
      The 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2016) Best Paper Awards (Domestic Track)
      科技部優秀年輕學者研究計畫2016-2018