Hardcover. An introduction to the theory and methods of robust statistics, providing students with practical methods for carrying out robust procedures in a variety of statistical contexts and explaining the advantages of these procedures. In addition, the text develops techniques and concepts likely to be useful in the future analysis of new statistical models and procedures. Emphasizing the concepts of breakdown point and influence functon of an estimator, it demonstrates the technique of expressing an estimator as a descriptive measure from which its influence function can be derived and then used to explore the efficiency and robustness properties of the estimator. Mathematical techniques are complemented by computational algorithms and Minitab macros for finding bootstrap and influence function estimates of standard errors of the estimators, robust confidence intervals, robust regression estimates and their standard errors. Includes examples and problems. An introduction to the theory and methods of robust statistics, which aims to illustrate the need for robust procedures in a variety of statistical contexts, and to develop the techniques and concepts useful in the analysis of new statistical models and procedures. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Robust Estimation and Testing (Hardcover)
Publisher: John Wiley & Sons Inc, New York
Publication Date: 1990
Language: English
ISBN 10: 0471855472
ISBN 13: 9780471855477
Binding: Hardcover
Edition: 1st Edition
Condition: new

About this title

Synopsis

An introduction to the theory and methods of robust statistics, providing students with practical methods for carrying out robust procedures in a variety of statistical contexts and explaining the advantages of these procedures. In addition, the text develops techniques and concepts likely to be useful in the future analysis of new statistical models and procedures. Emphasizing the concepts of breakdown point and influence functon of an estimator, it demonstrates the technique of expressing an estimator as a descriptive measure from which its influence function can be derived and then used to explore the efficiency and robustness properties of the estimator. Mathematical techniques are complemented by computational algorithms and Minitab macros for finding bootstrap and influence function estimates of standard errors of the estimators, robust confidence intervals, robust regression estimates and their standard errors. Includes examples and problems.

About the Author

Robert G. Staudte and Simon J. Sheather are the authors of Robust Estimation and Testing, published by Wiley.

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