Contemporary Experimental Design, Multivariate Analysis and Data Mining

1. Front Matter

   Pages i-xvii

   PDF

2. Review of Kai-Tai Fang’s Contribution

   1. Front Matter

      Pages 1-1

      PDF

   2. Walking on the Road to the Statistical Pyramid

      • Jianxin Pan, Jiajuan Liang, Guoliang Tian

      Pages 3-19

   3. The Contribution to Experimental Designs by Kai-Tai Fang

      • Min-Qian Liu, Dennis K. J. Lin, Yongdao Zhou

      Pages 21-35

   4. From “Clothing Standard” to “Chemometrics”

      • Ping He, Xiaoling Peng, Qingsong Xu

      Pages 37-48

   5. A Review of Prof. Kai-Tai Fang’s Contribution to the Education, Promotion, and Advancement of Statistics in China

      • Gang Li, Xiaoling Peng

      Pages 49-65

3. Design of Experiments

   1. Front Matter

      Pages 67-67

      PDF

   2. Is a Transformed Low Discrepancy Design Also Low Discrepancy?

      • Yiou Li, Lulu Kang, Fred J. Hickernell

      Pages 69-92

   3. The Construction of Optimal Design for Order-of-Addition Experiment via Threshold Accepting

      • Peter Winker, Jianbin Chen, Dennis K. J. Lin

      Pages 93-109

   4. Construction of Uniform Designs on Arbitrary Domains by Inverse Rosenblatt Transformation

      • Mei Zhang, Aijun Zhang, Yongdao Zhou

      Pages 111-126

   5. Drug Combination Studies, Uniform Experimental Design and Extensions

      • Ming T. Tan, Hong-Bin Fang

      Pages 127-144

   6. Modified Robust Design Criteria for Poisson Mixed Models

      • Hongyan Jiang, Rongxian Yue

      Pages 145-161

   7. Study of Central Composite Design and Orthogonal Array Composite Design

      • Si Qiu, Minyu Xie, Hong Qin, Jianhui Ning

      Pages 163-175

   8. Uniform Design on Manifold

      • Yu Tang

      Pages 177-186

4. Multivariate Analysis

   1. Front Matter

      Pages 187-187

      PDF

   2. An Application of the Theory of Spherical Distributions in Multiple Mean Comparison

      • Jiajuan Liang, Man-Lai Tang, Jing Yang, Xuejing Zhao

      Pages 189-199

   3. Estimating the Location Vector for Spherically Symmetric Distributions

      • Jian-Lun Xu

      Pages 201-215

   4. On Equidistant Designs, Symmetries and Their Violations in Multivariate Models

      • Milan Stehlík, Mirtha Pari Ruiz, Silvia Stehlíková, Ying Lu

      Pages 217-225

   5. Estimation of Covariance Matrix with ARMA Structure Through Quadratic Loss Function

      • Defei Zhang, Xiangzhao Cui, Chun Li, Jianxin Pan

      Pages 227-239

5. Data Mining

   1. Front Matter

      Pages 241-241

      PDF

The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences. Especially in the era of big data, researchers can easily collect data characterised by massive dimensions and complexity.

In celebration of Professor Kai-Tai Fang’s 80th birthday, we present this book, which furthers new and exciting developments in modern statistical theories, methods and applications. The book features four review papers on Professor Fang’s numerous contributions to the fields of experimental design, multivariate analysis, data mining and education. It also contains twenty research articles contributed by prominent and active figures in their fields. The articles cover a wide range of important topics such as experimental design, multivariate analysis, data mining, hypothesis testing and statistical models. 

• 62F 62H 62G 62K 62J 62N 62P
• big data
• Functional data
• Covariance matrix
• high-dimensional data
• multivariate data
• network data
• variable selection
• experimental design
• longitudinal data
• Robust design
• survival data
• machine learning
• data mining
• Composite design
• Quantile regression

• Department of Financial Engineering, Princeton University, Princeton, USA

  Jianqing Fan

• Department of Mathematics, The University of Manchester, Manchester, UK

  Jianxin Pan

Jianqing Fan is the Frederick L. Moore Professor at Princeton University.  After receiving his Ph.D. from the University of California at Berkeley, he was appointed a professor at the University of North Carolina at Chapel Hill (1989-2003), the University of California at Los Angeles (1997-2000), Chinese University of Hong Kong (2000-2003) and Princeton University (2003-present). A past president of the Institute of Mathematical Statistics and International Chinese Statistical Association, he is currently a co-editor of the Journal of Business and Economics Statistics and a former co-editor of Annals of Statistics, Probability Theory and Related Fields, and Journal of Econometrics. His published work on statistics, economics, finance, and computational biology has been recognized with the 2000 COPSS President’s Award, the 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellowship, P.L. Hsu Prize, Guy medal, Noether Senior Scholar Award. He is also member of the Academia Sinica, IMS, ASA, AAAS and SoFiE.

Jianxin Pan is a Professor of Statistics at the Department of Mathematics, The University of Manchester. He received his Ph.D. from Hong Kong Baptist University in 1996, and was a Research Associate at the Rothamsted Experiment Station (1996-1999) and the University of St Andrews (1999-2000). He was a Lecturer at Keele University (2000-2002), and has been a Professor of Statistics at the University of Manchester since 2006. He was the Head of the Probability and Statistics Group at the School of Mathematics (2009-2012), and has been a Fellow of the Royal Statistical Society and Elected Member of the International Statistical Institute, both since 2007. He is an Associate Editor for Biometrics (2008-2018), Biometrical Journal (2016-present) and Journal of Multivariate Analysis (2019-present), a Turing Fellow at The Alan Turing Institute for data science and artificial intelligence and Chair of the Royal Statistical Society Manchester Group.    

Policies and ethics