„The authors are to be congratulated for providing consulting statisticians and advanced students of statistics with an excellent guide to the rich methodology now available. Every statistician will benefit from having this book on their shelf, or, better yet, on their desk.“ (Australian & New Zealand Journal of Statistics, 2011)
„All treated methods are illustrated with several data examples. These data examples show clearly the superiority of the robust methods compared with the classical methods... However, since there exists a website with instructions for running the data examples of this book, the new robust methods can be easily applied.“ (Biometrical Journal, February 2011)„The book by Heritier et al. is the most comprehensive and practical discussion of robust methods to date. The combination of a summary of robust methods, extensive discussion of applications, and accompanying R code give this book the potential to increase the use of robust methods in practice.“ (Journal of Biopharmaceutical Statistics, March 2010)
Robust Methods in Biostatistics
von Stephane Heritier, Eva Cantoni, Samuel Copt und Maria-Pia Victoria-FeserRobust statistics is an extension of classical statistics thatspecifically takes into account the concept that the underlyingmodels used to describe data are only approximate. Its basicphilosophy is to produce statistical procedures which are stablewhen the data do not exactly match the postulated models as it isthe case for example with outliers.
Robust Methods in Biostatistics proposes robustalternatives to common methods used in statistics in general and inbiostatistics in particular and illustrates their use on manybiomedical datasets. The methods introduced include robustestimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models:
* Linear regression
* Generalized linear models
* Linear mixed models
* Marginal longitudinal data models
* Cox survival analysis model
The methods are introduced both at a theoretical and appliedlevel within the framework of each general class of models, with aparticular emphasis put on practical data analysis. This book is ofparticular use for research students, applied statisticians andpractitioners in the health field interested in more stablestatistical techniques. An accompanying website provides R code forcomputing all of the methods described, as well as for analyzingall the datasets used in the book.
Robust Methods in Biostatistics proposes robustalternatives to common methods used in statistics in general and inbiostatistics in particular and illustrates their use on manybiomedical datasets. The methods introduced include robustestimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models:
* Linear regression
* Generalized linear models
* Linear mixed models
* Marginal longitudinal data models
* Cox survival analysis model
The methods are introduced both at a theoretical and appliedlevel within the framework of each general class of models, with aparticular emphasis put on practical data analysis. This book is ofparticular use for research students, applied statisticians andpractitioners in the health field interested in more stablestatistical techniques. An accompanying website provides R code forcomputing all of the methods described, as well as for analyzingall the datasets used in the book.
