Introduction to Robust Estimation and Hypothesis Testing (Statistical Modeling and Decision Science)

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.


* Covers modern, more accurate methods of statistical estimation and hypothesis testing not covered in existing books
* Provides up-to-date test results dealing with heteroscedasticity
* Software built in S-PLUS is available free of charge via an anonymous ftp site
* Guides the reader through the foundations of robust methods