Robustness of Statistical Tests
- 208 pages
- English
- PDF
- Available on iOS & Android
Robustness of Statistical Tests
About This Book
Robustness of Statistical Tests provides a general, systematic finite sample theory of the robustness of tests and covers the application of this theory to some important testing problems commonly considered under normality. This eight-chapter text focuses on the robustness that is concerned with the exact robustness in which the distributional or optimal property that a test carries under a normal distribution holds exactly under a nonnormal distribution. Chapter 1 reviews the elliptically symmetric distributions and their properties, while Chapter 2 describes the representation theorem for the probability ration of a maximal invariant. Chapter 3 explores the basic concepts of three aspects of the robustness of tests, namely, null, nonnull, and optimality, as well as a theory providing methods to establish them. Chapter 4 discusses the applications of the general theory with the study of the robustness of the familiar Student's r-test and tests for serial correlation. This chapter also deals with robustness without invariance. Chapter 5 looks into the most useful and widely applied problems in multivariate testing, including the GMANOVA (General Multivariate Analysis of Variance). Chapters 6 and 7 tackle the robust tests for covariance structures, such as sphericity and independence and provide a detailed description of univariate and multivariate outlier problems. Chapter 8 presents some new robustness results, which deal with inference in two population problems. This book will prove useful to advance graduate mathematical statistics students.
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Table of contents
- Front Cover
- Robustness of Statistical Tests
- Copyright Page
- Table of Contents
- Dedication
- Preface
- Introduction
- Chapter 1. Spherically Symmetric Distributions
- Chapter 2. Invariance Approach to Testing
- Chapter 3. General Approach to the Robustness of Tests
- Chapter 4. Robustness of t-Test and Tests for Serial Correlation
- Chapter 5. General Multivariate Analysis of Variance(GMANOVA)
- Chapter 6. Tests for Covariance Structures
- Chapter 7. Detection of Outliers
- Chapter 8. Two-Population Problems
- References
- Author Index
- Subject Index