Robustness theory and application

Used to develop data analytical methods, which are resistant to outlying observations in the data, while capable of detecting outliers, robust statistics is extremely useful for solving an array of common problems, such as estimating location, scale, and regression parameters.

Written by an internationally recognised expert in the field of robust statistics, this book addresses a range of well-established techniques while exploring, in depth, new and exciting methodologies.

Local robustness and global robustness are discussed, and problems of non-identifiability and adaptive estimation are considered.

Rather than attempt an exhaustive investigation of robustness, the author provides readers with a timely review of many of the most important problems in statistical inference involving robust estimation, along with a brief look at confidence intervals for location.