Mixed Models: Theory and Applications with R: 893 (Wiley Series in Probability and Statistics) - Tapa dura

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EUGENE DEMIDENKO, PhD, is Professor of Biostatistics and Epidemiology at the Geisel School of Medicine and Department of Mathematics at Dartmouth College. Dr. Demidenko carries out collaborative work at the Thayer School of Engineering, Dartmouth College, including nanocancer therapy and electrical impedance tomography for breast cancer detection. Dr. Demidenko is recipient of several awards from the American Statistical Association and has been an invited lecturer at several institutes and academies around the world.

De la contraportada

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.

De la solapa interior

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.