Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image.

Paying special attention to algorithms and their implementations, the book 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.

About the Author

EUGENE DEMIDENKO, PhD, is presently Associate Professor of Biostatistics and Epidemiology at the Dartmouth (NH) Medical School. He received his PhD in Mathematics and Statistics from the Central Institute of Economics and Mathematics of the Academy of Sciences of the USSR. His research interests cover a broad range of theoretical and computational statistics as applicable to bioengineering and cancer-related areas. He has served as an invited lecturer to several institutes/academies around the world.

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