Mixed Models (eBook)

Praise for the First Edition

"This book will serve to greatly complement the growing
number of texts dealing with mixed models, and I highly recommend
including it in one's personal library."

--Journal of the American Statistical
Association



Mixed modeling is a crucial area of statistics, enabling
the analysis of clustered and longitudinal data. Mixed Models:
Theory and Applications with R, Second Edition fills a gap in
existing literature between mathematical and applied statistical
books by presenting a powerful examination of mixed model theory
and application with special attention given to the implementation
in R.

The new edition provides in-depth mathematical coverage of mixed
models' statistical properties and numerical algorithms, as
well as nontraditional applications, such as regrowth curves,
shapes, and images. The book features the latest topics in
statistics including modeling of complex clustered or longitudinal
data, modeling data with multiple sources of variation, modeling
biological variety and heterogeneity, Healthy Akaike Information
Criterion (HAIC), parameter multidimensionality, and statistics of
image processing.

Mixed Models: Theory and Applications with R, Second
Edition features unique applications of mixed model
methodology, as well as:

* Comprehensive theoretical discussions illustrated by examples
and figures

* Over 300 exercises, end-of-section problems, updated data sets,
and R subroutines

* Problems and extended projects requiring simulations in R
intended to reinforce material

* Summaries of major results and general points of discussion at
the end of each chapter

* Open problems in mixed modeling methodology, which can be used
as the basis for research or PhD dissertations

Ideal for graduate-level courses in mixed statistical modeling,
the book is also an excellent reference for professionals in a
range of fields, including cancer research, computer science, and
engineering.