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 regressionGeneralized linear modelsLinear mixed
modelsMarginal longitudinal data modelsCox 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.
General
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