Robust Methods in Biostatistics is popular PDF and ePub book, written by Stephane Heritier in 2009-05-11, it is a fantastic choice for those who relish reading online the Medical genre. Let's immerse ourselves in this engaging Medical book by exploring the summary and details provided below. Remember, Robust Methods in Biostatistics can be Read Online from any device for your convenience.
Robust Methods in Biostatistics Book PDF Summary
Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, 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 applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.
Detail Book of Robust Methods in Biostatistics PDF
- Author : Stephane Heritier
- Release : 11 May 2009
- Publisher : John Wiley & Sons
- ISBN : 047074054X
- Genre : Medical
- Total Page : 292 pages
- Language : English
- PDF File Size : 18,6 Mb
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