Mixed models : theory and applications

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Title:

Author:

Demidenko, E. Z. (Evgeniĭ Zi︠a︡movich)

ISBN:

9780471601616

Personal Author:

Demidenko, E. Z. (Evgeniĭ Zi︠a︡movich)

Publication Information:

Hoboken, N.J. : Wiley-Interscience, 2004.

Physical Description:

xviii, 704 p. : ill. ; 24 cm.

Series:

Wiley series in probability and statistics

Series Title:

Wiley series in probability and statistics

Contents:

1. Introduction : why mixed models? -- 2. MLE for LME model -- 3. Statistical properties of the LME model -- 4. Growth curve model and generalizations -- 5. Meta-analysis model -- 6. Nonlinear marginal model -- 7. Generalized linear mixed models -- 8. Nonlinear mixed effects model -- 9. Diagnostics and influence analysis -- 10. Tumor regrowth curves -- 11. Statistical analysis of shape -- 12. Statistical image analysis -- 13. App. : useful facts and formulas.

Abstract:

"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. 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 accessible for professionals and students alike in a broad range of fields such as cancer research, computer science, engineering, and industry."--BOOK JACKET.

Subject Term:

Analysis of variance.

Electronic Access:

Publisher description http://www.loc.gov/catdir/description/wiley042/2004045643.html
Table of contents http://www.loc.gov/catdir/toc/wiley041/2004045643.html

Copies:

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Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168020368474	519.538 D379M 2004	1	Searching... Unknown

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Summary

A rigorous, self-contained examination of mixed model theory and application

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.