Contemporary Experimental Design, Multivariate Analysis and Data Mining: Festschrift in Honour of Professor Kai-Tai Fang - Softcover

Inhaltsangabe

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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