
Introduction to Robust Estimation and Hypothesis Testing
Rand R. Wilcox(Author)
Academic Press
2nd Edition
Published on 22. January 2005
Book
Hardback
608 pages
978-0-12-751542-7 (ISBN)
Article exhausted; check for reprint
Description
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations.Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables.
Reviews / Votes
"...Wilcox has greatly enhanced this book, which is now almost twice as large as the first edition. This would now seem to be a good book for everyone to have in their library." -TECHNOMETRICS, VOL. 47, 2005More details
Edition
2nd edition
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
College/higher education
Advanced graduate students interested in applying cutting-edge methods for analyzing data. Also, any applied researcher who uses ANOVA or regression will benefit. A typical course would be Quantitative Methods found in Mathematics, Economics, Health and Biological Sciences and Psychology departments.
Edition type
New edition
Dimensions
Height: 229 mm
Width: 152 mm
Weight
880 gr
ISBN-13
978-0-12-751542-7 (9780127515427)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Rand R. Wilcox
Introduction to Robust Estimation and Hypothesis Testing
Book
10/2018
3rd Edition
Academic Press
€95.32
The article will not be published

Rand R. Wilcox
Introduction to Robust Estimation and Hypothesis Testing
Book
02/2012
3rd Edition
Academic Press
€107.70
Article exhausted; check for reprint
Additional editions

Rand R. Wilcox
Introduction to Robust Estimation and Hypothesis Testing
E-Book
01/2005
2nd Edition
Academic Press
€89.95
Available for download
Person
Rand R. Wilcox has a Ph.D. in psychometrics, and is a professor of psychology at the University of Southern California. Wilcox's main research interests are statistical methods, particularly robust methods for comparing groups and studying associations. He also collaborates with researchers in occupational therapy, gerontology, biology, education and psychology. Wilcox 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. Wilcox is the author of 12 books on statistics and has published many papers on robust methods. He is currently an Associate Editor for four statistics journals and has served on many editorial boards. He has given numerous invited talks and workshops on robust methods.
Content
1. Introductio
2. A Foundation for Robust Methods
3. Estimating Measures of Location and Scale
4. Confidence Intervals in the One-Sample Case
5. Comparing Two Groups
6. Some Multivariate Methods
7. One-Way and Higher Designs for Independent Groups
8. Comparing Multiple Dependent Groups
9. Correlation and Tests of Independence
10. Robust Regression
11. More Regression Methods
2. A Foundation for Robust Methods
3. Estimating Measures of Location and Scale
4. Confidence Intervals in the One-Sample Case
5. Comparing Two Groups
6. Some Multivariate Methods
7. One-Way and Higher Designs for Independent Groups
8. Comparing Multiple Dependent Groups
9. Correlation and Tests of Independence
10. Robust Regression
11. More Regression Methods