Introduction to Robust Estimation and Hypothesis Testing

Introduction to Robust Estimation and Hypothesis Testing

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Introduction to Robust Estimation and Hypothesis Testing focuses on the practical applications of modern, robust statistical methods. The increased accuracy and power of modern methods is remarkable compared tothe conventional approaches of the analysis of variance (ANOVA) and regression. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems withstandard methods that seemed insurmountable only a few years ago. This book provides a thorough, up-to-date explanation of the foundation of robust methods for beginners. It guides the reader through the basic strategies used for practical solutions to problems, and includes helpful updates which are available free of charge via an anonymous ftp site. The book also provides a brief background on the foundations of modern methods, placing the new methods in historical context.
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Product details

  • Hardback | 296 pages
  • 158.2 x 235.5 x 19.8mm | 612.11g
  • Academic Press Inc
  • San Diego, United States
  • English
  • index
  • 0127515453
  • 9780127515458

Table of contents

Practical Reasons for Using Robust Methods. A Foundation for Robust Methods. Estimating Measures of Location and Scale. Confidence Intervals in the One-Sample Case. Comparing Two Groups. One-Way and Higher Designs. Correlationand Related Issues. Robust Regression. More Regression Methods.Practical Reasons for Using Robust Methods: Problems with Assuming Normality. Transformations. The Influence Curve. Is the ANOVA F Robust? Regression. More Remarks. Using the Computer. A Foundation for Robust Methods: Basic Tools for Judging Robustness. Some Measures of Location and Their Influence Function. Measures of Scale. Scale Equivariant M-Measures of Location. Winsorized Expected Values. Estimating Measures of Location and Scale: The Sample Timmed Mean. The Finite Sample Breakdown Point. Estimating Quantiles. An M-Estimator of Location. One-Step M-estimator. W-estimators. Some Comparisons of the Locaiton Estimators. More Measures of Scale. Exercises. Confidence Intervals in the One-Sample Case: Problems When Working with Means. The g-and-h Distribution. Inferences About the Trimmed Mean. Inferences About M-Estimators. Confidence Intervals for Quantiles. Concluding Remarks. Exercises. Comparing Two Groups: The Shift Function. Student's Test. The Yuen-Welch Test. Comparing M-Estimators. Comparing Biweight Midvariances. Inferences about p. Comparing Dependent Groups. Exercises. One Way and Higher Dseigns: Trimmed Means in a One-Way Design. Multiple Comparisons and Linear Constrsts. A Random Effects Model forTrimmed Means. Comparing M-Measures of Location. A Ranked-Based Test. A One-Way Design with Dependent Groups. A split-Plot Design. Some Concluding Remarks. Exercises. Correlation and Related Issues: Problems with the Product Moemt Correlation. The Percentage Bend Correlation. The Biweight Midcovariance. Multivariate Measures of Location and Scatter. Minimum Volume Ellipsoid Estimator. Exercises. Robust Regression: Problems with Ordinary Least Squares. M-Estimator. The Hat Matrix. Generalized M-Estimators. The Coakley-Hettmansperger Estimator. A Criticism of Methods with a High Breakdown Point. The Biweight Midregression Method. Alternative Estimation Procedures. Exercises. More Regression Methods: Omnibus Tests for Regression Parameters.Comparing the Parameters of Two Independent Groups. Curvilinearity. ANCOVA. Exercises. Subject Index.
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Review quote

The two biggest strengths of Introduction to Robust Estimation and Hypothesis Testing, by Rand R. Wilcox, are its practical usability and its inherent structure....Introduction to Robust Estimation and Hypothesis Testing is a nice complement to other books in the area of robust methods. --Paul S. Horn, University of Cincinnati, TECHNOMETRICS
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About Rand R. Wilcox

Rand R. Wilcox has a Ph.D. in psychometrics, is a professor of psychology at the University of Southern California, and a Fellow of the Royal Statistics Society and American Psychological Society. He is an internationally recognized expert in the field of Applied Statistics and has concentrated much of his research in the area of ANOVA and Regression. He has authored twobooks and more than 130 journal articles.
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