Mixed Models: Theory and Applications
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 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
  • 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
  • Over 300 exercises, end-of-section problems, updated data sets, and R subroutines

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.

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State-of-the-art methodologies are discussed,
among them:

  • Linear mixed-effects model.
  • Linear growth curve model.
  • Generalized linear growth curve model.
  • Robust mixed model.
  • Models with linear covariance structure.
  • Meta-analysis model.
  • Models for binary and count clustered data (logistic, probit, Poisson).
  • Generalized estimating equations approach.
  • Nonlinear mixed model.

Chapter on diagnostics provides comprehensive introduction of linear and nonlinear statistical models. Special attention is given to I-influence analysis with lots of examples. Algorithms and their implementation are discussed in detail. Several appendices make the text self-contained. Innovative applications include tumor regrowth and statistical analysis of shapes and images.

The book also discusses:

  • Modeling of complex clustered or longitudinal data.
  • Modeling data with multiple sources of variation.
  • Modeling biological variety and heterogeneity.
  • Mixed model as a compromise between the frequentist and Bayesian approaches.
  • Mixed model for the penalized log-likelihood.
  • Healthy Akaike Information Criterion (HAIC).
  • How to cope with parameter multidimensionality.
  • How to solve ill-posed problems including image reconstruction problems.
  • Modeling of ensemble shapes and images.
  • Statistics of image processing.

Major results and points of discussion at the end of each chapter along with “Summary Points” sections make this reference not only comprehensive but also highly accessible for professionals and students alike in a broad range of fields such as cancer research, computer science, engineering, and industry.

This text may serve as a basis for a graduate course in statistics.

Copyright © 2013 Eugene Demidenko