Multiple Comparison Procedures

Informationen zum Autor Yosef Hochberg , PhD, is Professor in the Department of Statistics and Operations Research at Tel Aviv University, Israel. Dr. Hochberg's areas of research interest include multiple comparisons, categorical data analysis, and medical applications of biostatistical methods. Ajit C. Tamhane , PhD, is Senior Associate Dean of the McCormick School of Engineering and Applied Science at Northwestern University, where he is also Professor in the Department of Industrial Engineering and Management Sciences. A Fellow of the American Statistical Society, Dr. Tamhane has more than thirty years of academic and consulting experience in the areas of applied and mathematical statistics. He is the author of Statistical Analysis of Designed Experiments: Theory and Applications, also published by Wiley. Klappentext The Wiley Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.Offering a balanced, comprehensive presentation of the topic, Multiple Comparison Procedures refutes the belief held by some statisticians that such procedures have no place in data analysis. With equal emphasis on theory and applications, the authors introduce the advantages of multiple comparison techniques in reducing error rates and in ensuring the validity of statistical inferences. The book provides clear, detailed descriptions of the derivation and implementation of a variety of procedures, paying particular attention to classical approaches and confidence estimation procedures. Numerous examples and tables for implementing procedures are included, making this book both practical and informative. Zusammenfassung Multiple Comparison Procedures offers an up-to-date view of multiple comparison procedures, disproving the belief held by some statisticians that such procedures have no place in data analysis. Inhaltsverzeichnis 1. Introduction 1 1. Two Early Multiple Comparison Procedures 2 2. Basic Notions and Philosophy of Multiple Comparisons 5 3. Examples 12 Part I. Procedures Based in Classical Approaches for Fixed-Effects Linear Models with Normal Homoscedastic Independent Errors 17 2. Some Theory of Multiple Comparison Procedures for Fixed-Effects Linear Models 17 2. Single-Step Procedures for Nonhierarchical Families 28 3. Single-Step Procedures for Hierarchical Families 43 4. Step-Down Procedures 53 3. Single-Step Procedures for Pairwise and More General Comparisons among All Treatments 72 1. Scheffé's S-Procedure 73 2. Tukey's T-Procedure for Balanced Designs 80 3. Modifications of the T-Procedure for Unbalanced Designs 85 4. Comparisons among Single-Step Procedures 102 5. Additional Topics 107 4. Stepwise Procedures for Pairwise and More General Comparisons among All Treatments 110 1. Step-Down Procedures Based on F-Statistics 111 2. Step-Down Procedures Based in Studentized Range Statistics 114 3. Peritz's Closed Step-Down Procedure 121 4. Step-Up Procedures 124 5. A Comparison of Single-Step and Stepwise Procedures 128 5. Procedures for Some Nonhierarchical Finite Families of Comparisons 134 1. Orthogonal Comparisons 135 2. Comparisons with a Control 139 3. Comparisons with the "Best" Treatment 150 4. Two Miscellaneous Families 157 6. Designing Experiments for Multiple Comparisons 161 1. Single-Stage Procedures 163 2. Two-Stage Procedures 171 3. Incomplete Block Designs for Comparing Treatments with a Control 174 Part II. Procedures for...

Auteur

Yosef Hochberg, PhD, is Professor in the Department of Statistics and Operations Research at Tel Aviv University, Israel. Dr. Hochberg's areas of research interest include multiple comparisons, categorical data analysis, and medical applications of biostatistical methods. Ajit C. Tamhane, PhD, is Senior Associate Dean of the McCormick School of Engineering and Applied Science at Northwestern University, where he is also Professor in the Department of Industrial Engineering and Management Sciences. A Fellow of the American Statistical Society, Dr. Tamhane has more than thirty years of academic and consulting experience in the areas of applied and mathematical statistics. He is the author of Statistical Analysis of Designed Experiments: Theory and Applications, also published by Wiley.

Texte du rabat
The Wiley Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Offering a balanced, comprehensive presentation of the topic, Multiple Comparison Procedures refutes the belief held by some statisticians that such procedures have no place in data analysis. With equal emphasis on theory and applications, the authors introduce the advantages of multiple comparison techniques in reducing error rates and in ensuring the validity of statistical inferences. The book provides clear, detailed descriptions of the derivation and implementation of a variety of procedures, paying particular attention to classical approaches and confidence estimation procedures. Numerous examples and tables for implementing procedures are included, making this book both practical and informative.

Résumé
Multiple Comparison Procedures offers an up-to-date view of multiple comparison procedures, disproving the belief held by some statisticians that such procedures have no place in data analysis.

Contenu

1. Introduction 1 1. Two Early Multiple Comparison Procedures 2

2. Basic Notions and Philosophy of Multiple Comparisons 5

3. Examples 12

Part I. Procedures Based in Classical Approaches for Fixed-Effects Linear Models with Normal Homoscedastic Independent Errors 17

2. Some Theory of Multiple Comparison Procedures for Fixed-Effects Linear Models 17

2. Single-Step Procedures for Nonhierarchical Families 28

3. Single-Step Procedures for Hierarchical Families 43

4. Step-Down Procedures 53

3. Single-Step Procedures for Pairwise and More General Comparisons among All Treatments 72

1. Scheffé's S-Procedure 73

2. Tukey's T-Procedure for Balanced Designs 80

3. Modifications of the T-Procedure for Unbalanced Designs 85

4. Comparisons among Single-Step Procedures 102

5. Additional Topics 107

4. Stepwise Procedures for Pairwise and More General Comparisons among All Treatments 110

1. Step-Down Procedures Based on F-Statistics 111

2. Step-Down Procedures Based in Studentized Range Statistics 114

3. Peritz's Closed Step-Down Procedure 121

4. Step-Up Procedures 124

5. A Comparison of Single-Step and Stepwise Procedures 128

5. Procedures for Some Nonhierarchical Finite Families of Comparisons 134

1. Orthogonal Comparisons 135

2. Comparisons with a Control 139

3. Comparisons with the "Best" Treatment 150

4. Two Miscellaneous Families 157

6. Designing Experiments for Multiple Comparisons 161

1. Single-Stage Procedures 163

2. Two-Stage Procedures 171

3. Incomplete Block Designs for Comparing Treatments with a Control 174

Part II. Procedures for Other Models and Problems, and Procedures Based on Alternative Approaches 179

7. Procedures for One-Way Layouts with Unequal Variances 181

1. Single-Stage Procedures 182

2. Two-Stage Procedures 194

3. Step-Down Procedures 204

8. Procedures for Mixed Two-Way Layouts and Designs with Random Covariates 207

1. Procedures for One-Way Repeated Measures and Mixed Two-Way Designs 208

2. Procedures for Analysis of Covariance Designs with Random Covariates 219 …