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Generalized Linear Mixed Models: Modern Concepts, Methods and
Applications presents an introduction to linear modeling using the
generalized linear mixed model (GLMM) as an overarching conceptual
framework. For readers new to linear models, the book helps them
see the big picture. It shows how linear models fit with the rest
of the core statistics curriculum and points out the major issues
that statistical modelers must consider. Along with describing
common applications of GLMMs, the text introduces the essential
theory and main methodology associated with linear models that
accommodate random model effects and non-Gaussian data. Unlike
traditional linear model textbooks that focus on normally
distributed data, this one adopts a generalized mixed model
approach throughout: data for linear modeling need not be normally
distributed and effects may be fixed or random. With numerous
examples using SAS (R) PROC GLIMMIX, this book is ideal for
graduate students in statistics, statistics professionals seeking
to update their knowledge, and researchers new to the generalized
linear model thought process. It focuses on data-driven processes
and provides context for extending traditional linear model
thinking to generalized linear mixed modeling. See Professor Stroup
discuss the book.
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