Robust Methods in Biostatistics PDF
By:Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser
Published on 2009-05-11 by John Wiley & Sons
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.
This Book was ranked at 30 by Google Books for keyword Biostatistics.
Book ID of Robust Methods in Biostatistics's Books is a8bUjWJsTx8C, Book which was written byStephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feserhave ETAG "eiLYtA1qM1s"
Book which was published by John Wiley & Sons since 2009-05-11 have ISBNs, ISBN 13 Code is 9780470740545 and ISBN 10 Code is 047074054X
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Download Robust Methods in Biostatistics PDF
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