Robust statistical methods are particularly useful for obtaining good results from non-normally distributed data with outliers. This book is the second edition of a book focussed on applying robust statistical methods using R. It is a substantial update, with new chapters on multivariate models, large sample versus finite sample behavior, and measurement error models. The most significant update is that the computing component has been greatly improved with R code and examples now integrated throughout the book. The book is supplemented by a website with all code and data available.
Mathematical tools of robustness
Characteristics of robustness
Estimation of real parameter
Large sample and finite sample behavior of robust estimators
Robust and nonparametric procedures in measurement error models
Bibliography, Subject Index, Author Index