Understanding robust and exploratory data analysis. Repr.

*(English)*Zbl 0955.62008
Wiley Classics Library. New York, NY: Wiley. xx, 447 p. (2000).

This book originally grew out of a working group on exploratory data analysis in the Department of Statistics at Harvard University that began in the spring of 1977 and has involved students, faculty members, academic visitors, and others. The robust and resistant techniques that are discussed have considerable support in the statistical literature, both at a highly abstract mathematical level and in extensive Monte Carlo studies. This book provides the basis for an adequate understanding of these techniques using examples and a much reduced level of mathematical sophistication. By studying this book, the user will become more effective in handling robust and exploratory techniques, the student better able to understand them, and the teacher better able to explain them.

The intention of the authors is that each chapter is reasonably self-contained except for a few generally applicable techniques from the early chapters. Thus, each method is explained in conjunction with an example or two. Examples are generally small, and almost all rely on real data. They help to introduce the reader to techniques and illustrate why and when one method is preferable to another. A brief collection of exercises at the end of each chapter enables the reader to participate more directly in applying the techniques to other sets of data, establishing their properties, or extending them to new situations. The mathematical prerequisites for the reader are not high for most chapters. At the same time, mathematical sophistication is matched to the requirement for explaining each technique, and so the level rises especially in Chapters 8 and 11. The reader can omit them at first reading with no loss of continuity.

Contents: Stem-and-leaf displays. (J.D. Emerson and D.C. Hoaglin); 2. Letter values: A set of selected order statistics. (D.C. Hoaglin); 3. Boxplots and batch comparison. (J.D. Emerson and J. Strenio); 4. Transforming data. (J.D. Emerson and M.A. Stoto); 5. Resistant lines for \(y\) versus \(x\). (J.D. Emerson and D.C. Hoaglin); 6. Analysis of two-way tables by medians. (J.D. Emerson and D.C. Hoaglin); 7. Examining residuals. (C. Goodall); 8. Mathematical aspects of transformation. (J.D. Emerson); 9. Introduction to more refined estimators. (D.C. Hoaglin, F. Mosteller and J.W. Tukey); 10. Comparing location estimators: trimmed means, medians, and trimean. (J.L. Rosenberger and M. Gasko); 11. M-estimators of location: An outline of the theory. (C. Goodall); 12. Robust scale estimators and confidence intervals for location. (B. Iglewicz).

The intention of the authors is that each chapter is reasonably self-contained except for a few generally applicable techniques from the early chapters. Thus, each method is explained in conjunction with an example or two. Examples are generally small, and almost all rely on real data. They help to introduce the reader to techniques and illustrate why and when one method is preferable to another. A brief collection of exercises at the end of each chapter enables the reader to participate more directly in applying the techniques to other sets of data, establishing their properties, or extending them to new situations. The mathematical prerequisites for the reader are not high for most chapters. At the same time, mathematical sophistication is matched to the requirement for explaining each technique, and so the level rises especially in Chapters 8 and 11. The reader can omit them at first reading with no loss of continuity.

Contents: Stem-and-leaf displays. (J.D. Emerson and D.C. Hoaglin); 2. Letter values: A set of selected order statistics. (D.C. Hoaglin); 3. Boxplots and batch comparison. (J.D. Emerson and J. Strenio); 4. Transforming data. (J.D. Emerson and M.A. Stoto); 5. Resistant lines for \(y\) versus \(x\). (J.D. Emerson and D.C. Hoaglin); 6. Analysis of two-way tables by medians. (J.D. Emerson and D.C. Hoaglin); 7. Examining residuals. (C. Goodall); 8. Mathematical aspects of transformation. (J.D. Emerson); 9. Introduction to more refined estimators. (D.C. Hoaglin, F. Mosteller and J.W. Tukey); 10. Comparing location estimators: trimmed means, medians, and trimean. (J.L. Rosenberger and M. Gasko); 11. M-estimators of location: An outline of the theory. (C. Goodall); 12. Robust scale estimators and confidence intervals for location. (B. Iglewicz).

Reviewer: J.Antoch (Praha)

##### MSC:

62-07 | Data analysis (statistics) (MSC2010) |

62-06 | Proceedings, conferences, collections, etc. pertaining to statistics |

00B15 | Collections of articles of miscellaneous specific interest |

62F35 | Robustness and adaptive procedures (parametric inference) |