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In this book, an integrated introduction to the statistical
inference is provided from a frequentist likelihood-based
viewpoint. Classical results are presented together with recent
developments largely built upon ideas due to R.A. Fisher. After a
unified review of background material (statistical methods,
likelihood, data reductions, first-order asymptotics) and inference
in the presence of nuisance parameters (including
pseufo-likelihoods), a self-contained introduction is given to
exponential families, exponential dispersion models, generalized
linear models, and group families. Finally, basic results of
higher-order asymptotics are introduced (index notation, asymptotic
expansions for statistics and distributions, and major applications
to likelihood inference). The emphasis is more on general concepts
and methods than on regularity conditions. Many examples are given
for specific statistical models. Each chapter is supplemented with
exercises, problems and bibliographic notes. This volume can serve
as a textbook in intermediate-level undergraduate courses.
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