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最優因析設計理論(英) 版權信息
- ISBN：9787030776686
- 條形碼：9787030776686 ; 978-7-03-077668-6
- 裝幀：平裝-鎖線膠訂
- 冊數：暫無
- 重量：暫無
- 所屬分類：>>
最優因析設計理論(英) 內容簡介
試驗設計是近代科學發展的重要基礎理論之一。它研究不同條件下各種試驗的很優設計準則、構造和分析的理論與方法。為適應現代試驗的需要，作者于2006年開始建立了一個新的很優因子分析設計理論，包括很優性準則、很優設計構造，以及他們在各種不同設計類中的推廣。本書首先給出近代試驗設計，主要是多因子試驗設計的基本知識和數學基礎，接著從二水平對稱因子設計開始介紹了該理論的一些基本概念，包括AENP的提出、GMC準則的引進、GMC設計的構造等。書中對由AENP建立的GMC準則得到的設計與由WLP建立的MA型準則得到的兩類設計的優良性進行了詳細比較。利用AENP理論，還證明了過去已有的兩個準則MA和MEC（優選估計容量準則）得到的很優設計在只關心低階效應時是等價的。隨后的數章分別介紹了GMC理論在各類設計中的推廣和應用，包括分區組因析設計、裂區設計、混合水平因析設計、非正規因析設計、多水平因析設計、折衷設計、穩健參數設計，建立了各種情形的GMC準則。書中還給出了大量的很優設計表供實際應用。
最優因析設計理論(英) 目錄
Contents “統計與數據科學叢書”序 iPreface iii 1 Introduction 1 1.1 Factorial Designs and Factorial Effects 1 1.2 Fractional Factorial Designs 4 1.3 Optimality Criteria 9 1.3.1 Maximum Resolution Criterion 91.3.2 Minimum Aberration Criterion 10 1.3.3 Clear Effects Criterion 11 1.3.4 Maximum Estimation Capacity Criterion 121.4 Organization of the Book 132 General Minimum Lower-Order Confounding Criterion for 2n–m Designs 15 2.1 GMC Criterion 15 2.2 Relationship with MA Criterion 20 2.3 Relationship with CE Criterion 23 2.4 Relationship with MEC Criterion 25 Appendix A: GMC 2n–m Designs with m ？ 4 26 Appendix B: GMC 2n–m Designs with 16， 32， and 64 Runs 28 3 General Minimum Lower-Order Confounding 2n–m Designs 31 3.1 Some Preparation 31 3.1.1 Several Useful Results 31 3.1.2 Structure of Resolution IV Design with N /4 1 ？ n ？ N /2 34 3.2 GMC 2n–m Designs with n ？ 5N /16 1 39 3.2.1 Main Results and Examples 39 3.2.2 Proof of Theorem 3.10 40 3.3 GMC 2n–m Designs with 9N /32 1 ？ n ？ 5N /16 46 3.3.1 Main Results and Example 46 3.3.2 Outline of the Proof of Theorem 3.16 463.4 GMC 2n–m Designs with N /4 1 ？ n ？ 9N /32 47 3.4.1 Some Properties of MaxC2 2n–m Designs with n = N /4 1 47 3.4.2 GMC 2n–m Designs with N /4 1 3.4.3 Outline of the Proof of Theorem 3.23 50 3.5 When Do the MA and GMC Designs Differ? 51 4 General Minimum Lower-Order Confounding Blocked Designs 53 4.1 Two Kinds of Blocking Problems 53 4.2 GMC Criteria for Blocked Designs 54 4.3 Construction of B-GMC Designs 57 4.3.1 B-GMC 2n–m : 2r Designs with 5N /16 1 ？ n ？ N /2 58 4.3.2 B-GMC 2n–m : 2r Designs with n > N /2 63 4.3.3 Weak B-GMC 2n–m : 2r Designs 67 4.4 Construction of B1-GMC Designs 69 4.4.1 B1-GMC 2n–m : 2r Designs with n ？ 5N /16 1 70 4.4.2 B1-GMC 2n–m : 2r Designs with 9N /32 1 ？ n ？ 5N /16 72 4.4.3 B1-GMC 2n–m : 2r Designs with N /4 1 ？ n ？ 9N /32 73 4.5 Construction of B2-GMC Designs 75 4.5.1 B2-GMC 2n–m : 2r Designs with n ？ 5N /16 1 76 4.5.2 B2-GMC 2n–m : 2r Designs with N /4 1 ？ n ？ 5N /16 78 5 Factor Aliased and Blocked Factor Aliased Effect-Number Patterns 805.1 Factor Aliased Effect-Number Pattern of GMC Designs 80 5.1.1 Factor Aliased Effect-Number Pattern 805.1.2 The F-AENP of GMC Designs 83 5.1.3 Application of the F-AENP 87 5.2 Blocked Factor Aliased Effect-Number Pattern of B1-GMC Designs 89 5.2.1 Blocked Factor Aliased Effect-Number Pattern 89 5.2.2 The B-F-AENP of B1-GMC Designs 925.2.3 Applications of the B-F-AENP 99 6 General Minimum Lower-Order Confounding Split-plot Designs 102 6.1 Introduction 1026.2 GMC Criterion for Split-plot Designs 103 6.2.1 Comparison with MA-MSA-FFSP Criterion 105 6.2.2 Comparison with Clear Effects Criterion 110 6.3 WP-GMC Split-plot Designs 111 6.3.1 WP-GMC Criterion for Split-plot Designs 1116.3.2 Construction of WP-GMC Split-plot Designs 114 7 Partial Aliased Effect-Number Pattern and Compromise Designs 119 7.1 Introduction 119 7.2 Partial Aliased Effect-Number Pattern 121 7.3 Some General Results of Compromise Designs 124 7.4 Class One Compromise Designs 126 7.4.1 Largest Class One Clear Compromise Designs and Their Construction 126 7.4.2 Supremum f ？(q， n) and Construction of Largest Class One CCDs 127 7.4.3 Supremum n？(q， f ) and Construction of Largest Class One CCDs 130 7.4.4 Largest Class One Strongly Clear Compromise Designs 133 7.4.5 Class One General Optimal Compromise Designs 1377.5 Discussion 141 8 General Minimum Lower-Order Confounding Criteria for Robust Parameter Designs 147 8.1 Introduction 147 8.2 Selection of Optimal Regular Robust Parameter Designs 149 8.3 An Algorithm for Searching Optimal Arrays 155 9 General Minimum Lower-Order Confounding Criterion for sn–m Designs 1629.1 Introduction to sn–m Designs 162 9.2 GMC Criterion and Relationship with Other Criteria 166 9.3 GMC sn–m Designs Using Complementary Designs 174 9.4 B-GMC Criterion for Blocked sn–m Designs 178 10 General Minimum Lower-Order Confounding Criterion for Orthogonal Arrays 182 10.1 Introduction 182 10.2 ANOVA Models and Confounding Between Effects 183 10.3 Generalized AENP and GMC Criterion 187 10.4 Relationship with Other Criteria 189 10.5 Some G-GMC Designs 193 References 196 Index 206“統計與數據科學叢書”已出版書目 208
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