Mixed models theory and applications with r pdf

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mixed models theory and applications with r pdf

Demidenko E. Mixed Models: Theory and Applications with R [PDF] - Все для студента

You are currently using the site but have requested a page in the site. Would you like to change to the site? Eugene Demidenko. 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 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.
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Published 31.12.2018

HLM II: The General Linear Model and the Linear Mixed Model

ISBN 13: 9781118091579

It seems that you're in Germany. We have a dedicated site for Germany. This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis. The book is suitable for a course in a M.

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 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:. 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. Demidenko carries out collaborative work at the Thayer School of Engineering, Dartmouth College, including nanocancer therapy and electrical impedance tomography for breast cancer detection.

Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as:. 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. 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.

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  2. Linear and Generalized Linear Mixed Models and Their Applications | Jiming Jiang | Springer

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