Fortunato Pesarin, Luigi Salmaso

Permutation Tests for Complex Data

• Gebundenes Buch

Produktbeschreibung

This revised and updated Second Edition builds upon the theoretical material presented in the first edition and illustrates how the methodology is relevant to modern statistical problems. The first three chapters focus on the recent theoretical advances within univariate and multivariate permutation tests, bringing the reader completely up to date with current thinking. Within the subsequent chapters, a greater practical emphasis is achieved through the inclusion of extensive software codes (in MATLAB and R) and worked examples, using real case studies from both experimental and observational studies.

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Complex multivariate testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. As a result, modern statistics needs permutation testing for complex data with low sample size and many variables, especially in observational studies.
The Authors give a general overview on permutation tests with a focus on recent theoretical advances within univariate and multivariate complex permutation testing problems, this book brings the reader completely up to date with today's current thinking.
Key Features:
Examines the most up-to-date methodologies of univariate and multivariate permutation testing.
Includes extensive software codes in MATLAB, R and SAS, featuring worked examples, and uses real case studies from both experimental and observational studies.
Includes a standalone free software NPC Test Release 10 with a graphical interface which allows practitioners from every scientific field to easily implement almost all complex testing procedures included in the book.
Presents and discusses solutions to the most important and frequently encountered real problems in multivariate analyses.
A supplementary website containing all of the data sets examined in the book along with ready to use software codes.
Together with a wide set of application cases, the Authors present a thorough theory of permutation testing both with formal description and proofs, and analysing real case studies. Practitioners and researchers, working in different scientific fields such as engineering, biostatistics, psychology or medicine will benefit from this book.

Produktdetails

• Wiley Series in Probability and Statistics .
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 448
• Erscheinungstermin: 3. Mai 2010
• Englisch
• Abmessung: 250mm x 175mm x 29mm
• Gewicht: 876g
• ISBN-13: 9780470516416
• ISBN-10: 0470516410
• Artikelnr.: 28162449

Autorenporträt

Fortunato Pesarin, Department of Statistics, University of Padova, Italy Professor Pesarin has been actively involved in the areas of multidimensional testing and permutation for almost 40 years, and is the author of dozens of publications in numerous international journals. Luigi Salmaso, Department of Management and Engineering, University of Padova, Italy Within the last 10 years, Dr Salmaso has amassed a large number of published articles, in a variety of journals, and has taught a number of courses in statsistics and nonparametric methods.

Inhaltsangabe

Contents Preface Notation and Abbreviations 1 Introduction 1.1 On
Permutation Analysis 1.2 The Permutation Testing Principle 1.3 Permutation
Approaches 1.4 When and Why Conditioning Is Appropriate 1.5 Randomization
and Permutation 1.6 Computational Aspects 1.7 Basic Notation 1.8 A Problem
with Paired Observations 1.9 The Permutation Solution 1.10 A Two-Sample
Problem 1.11 One-Way ANOVA 2 Theory of One-Dimensional Permutation Tests
2.1 Introduction 2.2 Definition of Permutation Tests 2.3 Some Useful Test
Statistics 2.4 Equivalence of Permutation Statistics 2.5 Arguments for
Selecting Permutation Tests 2.6 Examples of One-Sample Problems 2.7
Examples of Multi-sample Problems 2.8 Analysis of Ordered Categorical
Variables 2.9 Problems and Exercises 3 Further Properties of Permutation
Tests 3.1 Unbiasedness of Two-sample Tests 3.2 Power Functions of
Permutation Tests 3.3 Consistency of Permutation Tests 3.4 Permutation
Confidence Interval for ¿ 3.5 Extending Inference from Conditional to
Unconditional 3.6 Optimal Properties 3.7 Some Asymptotic Properties 3.8
Permutation Central Limit Theorems 3.9 Problems and Exercises 4 The
Nonparametric Combination Methodology 4.1 Introduction 4.2 The
Nonparametric Combination Methodology 4.3 Consistency, Unbiasedness and
Power of Combined Tests 4.4 Some Further Asymptotic Properties 4.5
Finite-Sample Consistency 4.6 Some Examples of Nonparametric Combination
4.7 Comments on the Nonparametric Combination 5 Multiple Testing Problems
and Multiplicity Adjustment 5.1 Defining Raw and Adjusted p-Values 5.2
Controlling for Multiplicity 5.3 Multiple Testing 5.4 The Closed Testing
Approach 5.5 Mult Data Example 5.6 Washing Test Data 5.7 Weighted Methods
for Controlling FWE and FDR 5.8 Adjusting Stepwise p-Values 6 Analysis of
Multivariate Categorical Variables 6.1 Introduction 6.2 The Multivariate
McNemar Test 6.3 Multivariate Goodness-of-Fit Testing for Ordered Variables
6.4 MANOVA with Nominal Categorical Data 6.5 Stochastic Ordering 6.6
Multifocus Analysis 6.7 Isotonic Inference 6.8 Test on Moments for Ordered
Variables 6.9 Heterogeneity Comparisons 6.10 Application to PhD Programme
Evaluation Using SAS 7 Permutation Testing for Repeated Measurements 7.1
Introduction 7.2 Carry-Over Effects in Repeated Measures Designs 7.3
Modelling Repeated Measurements 7.4 Testing Solutions 7.5 Testing for
Repeated Measurements with Missing Data 7.6 General Aspects of Permutation
Testing with Missing Data 7.7 On Missing Data Processes 7.8 The Permutation
Approach 7.9 The Structure of Testing Problems 7.10 Permutation Analysis of
Missing Values 7.11 Germina Data: An Example of an MNAR Model 7.12
Multivariate Paired Observations 7.13 Repeated Measures and Missing Data
7.14 Botulinum Data 7.15 Waterfalls Data 8 Some Stochastic Ordering
Problems 8.1 Multivariate Ordered Alternatives 8.2 Testing for Umbrella
Alternatives 8.3 Analysis of Experimental Tumour Growth Curves 8.4 Analysis
of PERC Data 9 NPC Tests for Survival Analysis 9.1 Introduction and Main
Notation 9.2 Comparison of Survival Curves 9.3 An Overview of the
Literature 9.4 Two NPC Tests 9.5 An Application to a Biomedical Study 10
NPC Tests in Shape Analysis 10.1 Introduction 10.2 A Brief Overview of
Statistical Shape Analysis 10.3 Inference with Shape Data 10.4 NPC Approach
to Shape Analysis 10.5 NPC Analysis with Correlated Landmarks 10.6 An
Application to Mediterranean Monk Seal Skulls 11 Multivariate Correlation
Analysis and Two-Way ANOVA 11.1 Autofluorescence Case Study 11.2 Confocal
Case Study 11.3 Two-Way (M)ANOVA 12 Some Case Studies Using NPC Test R. 10
and SAS Macros 12.1 An Integrated Approach to Survival Analysis in
Observational Studies 12.2 Integrating Propensity Score and NPC Testing
12.3 Further Applications with NPC Test R. 10 and SAS Macros 12.4 A
Comparison of Three Survival Curves 12.5 Survival Analysis Using NPC Test
and SAS 12.6 Logistic Regression and NPC Test for Multivariate Analysis
References Index