Robustness of Statistical Tests. Statistical Modeling and Decision Science

Kariya, Takeaki and Bimal K. Sinha:

ISBN 10: 0123982308 / ISBN 13: 9780123982308

Published by Academic Press, 1989

About this Item

189 Seiten; Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber. Es befindet sich neben dem Rückenschild lediglich ein Bibliotheksstempel im Buch; ordnungsgemäß entwidmet. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 520.

Seller Inventory # 2102360

Bibliographic Details

• Title: Robustness of Statistical Tests. Statistical Modeling and Decision Science
• Publisher: Academic Press
• Publication Date: 1989
• Language: English
• ISBN 10: 0123982308
• ISBN 13: 9780123982308
• Binding: gebundene Ausgabe
• Condition: Gut
• Weight: 520 grams
• Seller catalogs:
  • 200 Mathematik

About this title

Synopsis

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.