This book provides a systematic account of robust statistical methods, an area where the existing literature is dated, narrow, or treated in an overly theoretical manner. The authors discuss the entire range of robust statistical methods at an accessible level appropriate for students at a Master's level or beyond. The treatment covers differentiable statistical functions, influence functions, asymptotic distributions, and much more. It also provides numerous examples and exercises, as well as computational algorithms using the R software package for applications of robust statistical methods. Outstanding for course work, this text is also a valuable reference for statisticians and quantitative scientists.
Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on practical application.
The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands-on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It also includes a brief overview of R in an appendix for those with little experience using the software.
Based on more than a decade of teaching and research experience, Robust Statistical Methods with R offers a thorough, detailed overview of robust procedures. It is an ideal introduction for those new to the field and a convenient reference for those who apply robust methods in their daily work.
