A rigorious, self-contained examination of mixed model theory and its applications including regrowth curves, shape and image analysis.
This text provides the most complete coverage of mixed modeling methodology.
This book is for people who want to know mixed models inside-out.
State-of-the-art methodologies are discussed,
From a book review:
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.