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Contemporary experimental design, mu...

Fan, Jianqing.

Contemporary experimental design, multivariate analysis and data miningfestschrift in honour of Professor Kai-Tai Fang /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Contemporary experimental design, multivariate analysis and data miningedited by Jianqing Fan, Jianxin Pan.
其他題名:	festschrift in honour of Professor Kai-Tai Fang /
其他作者:	Fan, Jianqing.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xvii, 386 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Statistics.
電子資源:	https://doi.org/10.1007/978-3-030-46161-4
ISBN:	9783030461614$q(electronic bk.)

Contemporary experimental design, multivariate analysis and data miningfestschrift in honour of Professor Kai-Tai Fang /
Contemporary experimental design, multivariate analysis and data miningfestschrift in honour of Professor Kai-Tai Fang /[electronic resource] :edited by Jianqing Fan, Jianxin Pan. - Cham :Springer International Publishing :2020. - xvii, 386 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030461614$q(electronic bk.)

Standard No.: 10.1007/978-3-030-46161-4doiSubjects--Topical Terms:

182057
Statistics.

LC Class. No.: QA276.4 / .C668 2020

Dewey Class. No.: 519.5

Contemporary experimental design, multivariate analysis and data miningfestschrift in honour of Professor Kai-Tai Fang /
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505 0 $a 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.
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