Contemporary Experimental Design, Multivariate Analysis and Data Mining

Table of Contents

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   Book Overview
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   Chapter 1 Walking on the Road to the Statistical Pyramid
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   Chapter 2 The Contribution to Experimental Designs by Kai-Tai Fang
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   Chapter 3 From “Clothing Standard” to “Chemometrics”
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   Chapter 4 A Review of Prof. Kai-Tai Fang’s Contribution to the Education, Promotion, and Advancement of Statistics in China
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   Chapter 5 Is a Transformed Low Discrepancy Design Also Low Discrepancy?
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   Chapter 6 The Construction of Optimal Design for Order-of-Addition Experiment via Threshold Accepting
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   Chapter 7 Construction of Uniform Designs on Arbitrary Domains by Inverse Rosenblatt Transformation
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   Chapter 8 Drug Combination Studies, Uniform Experimental Design and Extensions
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    Chapter 9 Modified Robust Design Criteria for Poisson Mixed Models
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    Chapter 10 Study of Central Composite Design and Orthogonal Array Composite Design
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    Chapter 11 Uniform Design on Manifold
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    Chapter 12 An Application of the Theory of Spherical Distributions in Multiple Mean Comparison
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    Chapter 13 Estimating the Location Vector for Spherically Symmetric Distributions
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    Chapter 14 On Equidistant Designs, Symmetries and Their Violations in Multivariate Models
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    Chapter 15 Estimation of Covariance Matrix with ARMA Structure Through Quadratic Loss Function
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    Chapter 16 Depth Importance in Precision Medicine (DIPM): A Tree and Forest Based Method
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    Chapter 17 Bayesian Mixture Models with Weight-Dependent Component Priors
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    Chapter 18 Cosine Similarity-Based Classifiers for Functional Data
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    Chapter 19 Projection Test with Sparse Optimal Direction for High-Dimensional One Sample Mean Problem
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    Chapter 20 Goodness-of-fit Tests for Correlated Bilateral Data from Multiple Groups
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    Chapter 21 A Bilinear Reduced Rank Model
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    Chapter 22 Simultaneous Multiple Change Points Estimation in Generalized Linear Models
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    Chapter 23 Data-Based Priors for Bayesian Model Averaging
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    Chapter 24 Quantile Regression with Gaussian Kernels