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This is the second edition of a monograph on generalized linear
models with random effects that extends the classic work of
McCullagh and Nelder. It has been thoroughly updated, with around
80 pages added, including new material on the extended likelihood
approach that strengthens the theoretical basis of the methodology,
new developments in variable selection and multiple testing, and
new examples and applications. It includes an R package for all the
methods and examples that supplement the book.
This is the second edition of a monograph on generalized linear
models with random effects that extends the classic work of
McCullagh and Nelder. It has been thoroughly updated, with around
80 pages added, including new material on the extended likelihood
approach that strengthens the theoretical basis of the methodology,
new developments in variable selection and multiple testing, and
new examples and applications. It includes an R package for all the
methods and examples that supplement the book.
Based on a course in the theory of statistics this text
concentrates on what can be achieved using the likelihood/Fisherian
method of taking account of uncertainty when studying a statistical
problem. It takes the concept ot the likelihood as providing the
best methods for unifying the demands of statistical modelling and
the theory of inference. Every likelihood concept is illustrated by
realistic examples, which are not compromised by computational
problems. Examples range from a simile comparison of two accident
rates, to complex studies that require generalised linear or
semiparametric modelling. The emphasis is that the likelihood is
not simply a device to produce an estimate, but an important tool
for modelling. The book generally takes an informal approach, where
most important results are established using heuristic arguments
and motivated with realistic examples. With the currently available
computing power, examples are not contrived to allow a closed
analytical solution, and the book can concentrate on the
statistical aspects of the data modelling. In addition to classical
likelihood theory, the book covers many modern topics such as
generalized linear models and mixed models, non parametric
smoothing, robustness, the EM algorithm and empirical likelihood.
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