Details
Advanced Analysis of Variance
Wiley Series in Probability and Statistics 1. Aufl.
108,99 € 

Verlag:  Wiley 
Format:  
Veröffentl.:  19.07.2017 
ISBN/EAN:  9781119303343 
Sprache:  englisch 
Anzahl Seiten:  432 
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Beschreibungen
Introducing a revolutionary new model for the statistical analysis of experimental data In this important book, internationally acclaimed statistician, Chihiro Hirotsu, goes beyond classical analysis of variance (ANOVA) model to offer a unified theory and advanced techniques for the statistical analysis of experimental data. Dr. Hirotsu introduces the groundbreaking concept of advanced analysis of variance (AANOVA) and explains how the AANOVA approach exceeds the limitations of ANOVA methods to allow for global reasoning utilizing special methods of simultaneous inference leading to individual conclusions. Focusing on normal, binomial, and categorical data, Dr. Hirotsu explores ANOVA theory and practice and reviews current developments in the field. He then introduces three new advanced approaches, namely: testing for equivalence and noninferiority; simultaneous testing for directional (monotonic or restricted) alternatives and changepoint hypotheses; and analyses emerging from categorical data. Using realworld examples, he shows how these three recognizable families of problems have important applications in most practical activities involving experimental data in an array of research areas, including bioequivalence, clinical trials, industrial experiments, pharmacostatistics, and quality control, to name just a few. • Written in an expository style which will encourage readers to explore applications for AANOVA techniques in their own research • Focuses on dealing with real data, providing realworld examples drawn from the fields of statistical quality control, clinical trials, and drug testing • Describes advanced methods developed and refined by the author over the course of his long career as research engineer and statistician • Introduces advanced technologies for AANOVA data analysis that build upon the basic ANOVA principles and practices Introducing a breakthrough approach to statistical analysis which overcomes the limitations of the ANOVA model, Advanced Analysis of Variance is an indispensable resource for researchers and practitioners working in fields within which the statistical analysis of experimental data is a crucial research component. Chihiro Hirotsu is a Senior Researcher at the Collaborative Research Center, Meisei University, and Professor Emeritus at the University of Tokyo. He is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and he has been awarded the Japan Statistical Society Prize (2005) and the Ouchi Prize (2006). His work has been published in Biometrika, Biometrics, and Computational Statistics & Data Analysis, among other premier research journals.
Contents Preface Notation and abbreviations Chapter 1: introduction to design and analysis of experiments 1.1 Why simultaneous experiments? 1.2 Interaction effects 1.3 Choice of factors and their levels 1.4 Classification of factors 1.5 Fixed or random effects model? 1.6 Fisher’s three principles of experiments versus noise factor 1.7 Generalized interaction 1.8 Immanent problems in the analysis of interaction effects 1.9 Classification of factors in the analysis of interaction effects 1.10 Pseudo interaction effects (Simpson’s paradox) in categorical data 1.11 Upper bias by statistical optimization 1.12 Stage of experiments —exploratory, explanatory or confirmatory? — References Chapter 2: Estimation Theory 2.1 Best linear unbiased estimator (BLUE) 2.2 General minimum variance unbiased estimator 2.3 Efficiency of unbiased estimator 2.4 Linear model 2.5 Least squares (LS) method 2.6 Maximum likelihood estimator (MLE) 2.7 Sufficient statistics References Chapter 3: Basic Test Theory 3.1 Normal mean?@ 3.2 Normal variance 3.3 Confidence interval 3.4 Test theory in the linear model 3.5 Likelihood ratio test and efficient score test References Chapter 4: Multiple decision processes and accompanying confidence region 4.1 Introduction 4.2 Determining the sign of a normal mean —Unification of one and twosided tests— 4.3 An improved confidence region Reference Chapter 5: TwoSample Problem 5.1 Normal theory 5.2 Nonparametric tests 5.3 Unifying approach to noninferiority, equivalence and superiority tests References Chapter 6: OneWay Layout, Normal Model 6.1 Analysis of variance (ANOVA, overall F test) 6.2 Testing the equality of variances 6.3 Linear score test (nonparametric test) 6.4 Multiple comparisons 6.5 Directional tests References Chapter 7: OnwWay Layout, Binomial Populations 7.1 Introduction 7.2 Multiple comparisons 7.3 Directional tests References Chapter 8: Poisson Process 8.1 Max acc. t1 for the monotone and step changepoint hypotheses 8.2 Max acc. t2 for the convex and slope changepoint hypotheses Reference Chapter 9: Block Experiments 9.1 Complete randomized blocks 9.2 Balanced incomplete blocks 9.3 Nonparametric method in block experiments References Chapter 10: TwoWay Layout, Normal Model 10.1 Introduction 10.2 Overall analysis of variance (ANOVA) of twoway data 10.3 Rowwise multiple comparisons 10.4 Directional inference 10.5 Easy method for unbalanced data References Chapter 11: Analysis of TwoWay Categorical Data 11.1 Introduction 11.2 Overall goodnessoffit chisquare 11.3 Rowwise multiple comparisons 11.4 Directional inference in the case of natural ordering only in columns 11.5 Analysis of ordered rows and columns References Chapter 12: Mixed and Random Effects Model 12.1 Oneway random effects model 12.2 Twoway random effects model 12.3 Twoway mixed effect model 12.4 General linear mixed effects model References Chapter 13: Profile Analysis of Repeated Measurements 13.1 Comparing treatments based on up or downward profiles 13.2 Profile analysis of 24hours measurements of blood pressure References Chapter 14: Analysis of ThreeWay Categorical Data 14.1Analysis of threeway response data 14.2 Oneway experiment with twoway categorical responses 14.3 Twoway experiment with oneway categorical responses References Chapter 15: Design and Analysis of Experiments by Orthogonal Arrays 15.1 Experiments by an orthogonal array 15.2 Ordered categorical responses in a highly fractional experiment 15.3 Optimality of an orthogonal array References Appendix Index
Chihiro Hirotsu is a Senior Researcher at the Collaborative Research Center, Meisei University, and Professor Emeritus at the University of Tokyo. He is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and he has been awarded the Japan Statistical Society Prize (2005) and the Ouchi Prize (2006). His work has been published in Biometrika, Biometrics, and Computational Statistics & Data Analysis, among other premier research journals.
Introducing a revolutionary new model for the statistical analysis of experimental data In this important book, internationally acclaimed statistician, Chihiro Hirotsu, goes beyond classical analysis of variance (ANOVA) model to offer a unified theory and advanced techniques for the statistical analysis of experimental data. Dr. Hirotsu introduces the groundbreaking concept of advanced analysis of variance (AANOVA) and explains how the AANOVA approach exceeds the limitations of ANOVA methods to allow for global reasoning utilizing special methods of simultaneous inference leading to individual conclusions. Focusing on normal, binomial, and categorical data, Dr. Hirotsu explores ANOVA theory and practice and reviews current developments in the field. He then introduces three new advanced approaches, namely: testing for equivalence and noninferiority; simultaneous testing for directional (monotonic or restricted) alternatives and changepoint hypotheses; and analyses emerging from categorical data. Using realworld examples, he shows how these three recognizable families of problems have important applications in most practical activities involving experimental data in an array of research areas, including bioequivalence, clinical trials, industrial experiments, pharmacostatistics, and quality control, to name just a few. • Written in an expository style which will encourage readers to explore applications for AANOVA techniques in their own research • Focuses on dealing with real data, providing realworld examples drawn from the fields of statistical quality control, clinical trials, and drug testing • Describes advanced methods developed and refined by the author over the course of his long career as research engineer and statistician • Introduces advanced technologies for AANOVA data analysis that build upon the basic ANOVA principles and practices Introducing a breakthrough approach to statistical analysis which overcomes the limitations of the ANOVA model, Advanced Analysis of Variance is an indispensable resource for researchers and practitioners working in fields within which the statistical analysis of experimental data is a crucial research component. Chihiro Hirotsu is a Senior Researcher at the Collaborative Research Center, Meisei University, and Professor Emeritus at the University of Tokyo. He is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and he has been awarded the Japan Statistical Society Prize (2005) and the Ouchi Prize (2006). His work has been published in Biometrika, Biometrics, and Computational Statistics & Data Analysis, among other premier research journals.
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