Contemporary Experimental Design, Multivariate Analysis and Data Mining
Springer International Publishing (Verlag)
978-3-030-46160-7 (ISBN)
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.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), 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, 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, University of Manchester. He received his Ph.D. from Hong Kong Baptist University in 1996, and was a PDRA at the Rothamsted Experiment Station (1996-1999) and Research Associate at the University of St Andrews (1999-2000). He was a Lecturer at Keele University (2000-2002) and has been a full 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. A former / current Associate Editor for Biometrics (2008-2018), Biometrical Journal (2016-present) and Journal of Multivariate Analysis (2019- present), he is also a Turing Fellow at the Turing Institute in London and Chair of the Royal Statistical Society Manchester Group.
Part I Review of Kai-Tai Fang's Contribution.- 1 Walking on the Road to the Statistical Pyramid.- 2 The contribution to experimental designs by Kai-Tai Fang.- 3 From "Clothing Standard" to "Chemometrics".- 4 A Review of Professor Kai-Tai Fang's Contribution to the Education, Promotion, and Advancement of Statistics in China.- Part II Design of Experiments.- 5 Is a Transformed Low Discrepancy Design Also Low Discrepancy?.- 6 The construction of optimal design for order-of-addition experiment via threshold accepting.- 7 Construction of Uniform Designs on Arbitrary Domains by Inverse Rosenblatt Transformation.- 8 Drug Combination Studies, Uniform Experimental Design and Extensions.- 9 Modified robust design criteria for Poisson mixed models.- 10 Study of Central Composite Design and Orthogonal Array Composite Design.- 11 Uniform design on manifold.- Part III Multivariate Analysis.- 12 An Application of the Theory of Spherical Distributions in Multiple Mean Comparison.- 13 Estimating the Location Vector for Spherically Symmetric Distributions.- 14 On equidistant designs, symmetries and their violations in multivariate models.- 15 Estimation of covariance matrix with ARMA structure through quadratic loss function.- Part IV Data Mining.- 16 Depth Importance in Precision Medicine (DIPM): A Tree and Forest Based Method.- 17 Bayesian Mixture Models with Weight-Dependent Component Priors.- 18 Cosine Similarity-Based Classifiers for Functional Data.- Part V Hypothesis Test and Statistical Models.- 19 Projection Test with Sparse Optimal Direction for High-dimensional One Sample Mean Problem.- 20 Goodness-of-fit tests for correlated bilateral data from multiple groups.- 21 A Bilinear Reduced Rank Model.- 22 Simultaneous multiple change points estimation in generalized linear models.- 23 Data-Based Priors for Bayesian Model Averaging.- 24 Quantile regression with Gaussian kernels.
| Erscheinungsdatum | 24.05.2020 |
|---|---|
| Zusatzinfo | XVII, 386 p. 42 illus., 30 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 763 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Schlagworte | 62F 62H 62G 62K 62J 62N 62P • Big Data • Composite design • Covariance matrix • Data Mining • Experimental Design • Functional Data • high-dimensional data • longitudinal data • machine learning • multivariate data • network data • Quantile Regression • Robust Design • Survival Data • Variable selection |
| ISBN-10 | 3-030-46160-2 / 3030461602 |
| ISBN-13 | 978-3-030-46160-7 / 9783030461607 |
| Zustand | Neuware |
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