Mixed Models: Theory and Applications with R

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.

Avainsanat: analysis of tumor regrowth, model simulations, R subroutines, Healthy Akaike Information Criterion, HAIC, parameter multidimensionality, statistics of image processing, analysis of clustered data, modeling biological variety, modeling of longitudinal data, modeling of clustered data, Biostatistics, Medical Sciences Special Topics, Biostatistics, Medical Sciences Special Topics