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Mixed modelling is very useful, and easier than youthink!
Mixed modelling is now well established as a powerful approachto statistical data analysis. It is based on the recognition ofrandom-effect terms in statistical models, leading to inferencesand estimates that have much wider applicability and are morerealistic than those otherwise obtained.
Introduction to Mixed Modelling leads the readerinto mixed modelling as a natural extension of two more familiarmethods, regression analysis and analysis of variance. It providespractical guidance combined with a clear explanation of theunderlying concepts.
Like the first edition, this new edition shows diverseapplications of mixed models, provides guidance on theidentification of random-effect terms, and explains how to obtainand interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including thefollowing:
* Use of the software SAS, in addition to GenStat and R.
* Meta-analysis and the multiple testing problem.
* The Bayesian interpretation of mixed models.
Including numerous practical exercises with solutions, this bookprovides an ideal introduction to mixed modelling for final yearundergraduate students, postgraduate students and professionalresearchers. It will appeal to readers from a wide range ofscientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography.
Praise for the first edition:
„One of the main strengths of the text is the bridge itprovides between traditional analysis of variance and regressionmodels and the more recently developed class of mixed models... Eachchapter is well-motivated by at least one carefully chosenexample... demonstrating the broad applicability of mixed models inmany different disciplines... most readers will likely learnsomething new, and those previously unfamiliar with mixed modelswill obtain a solid foundation on thistopic.“--Kerrie Nelson University ofSouth Carolina, in American Statistician, 2007
Mixed modelling is now well established as a powerful approachto statistical data analysis. It is based on the recognition ofrandom-effect terms in statistical models, leading to inferencesand estimates that have much wider applicability and are morerealistic than those otherwise obtained.
Introduction to Mixed Modelling leads the readerinto mixed modelling as a natural extension of two more familiarmethods, regression analysis and analysis of variance. It providespractical guidance combined with a clear explanation of theunderlying concepts.
Like the first edition, this new edition shows diverseapplications of mixed models, provides guidance on theidentification of random-effect terms, and explains how to obtainand interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including thefollowing:
* Use of the software SAS, in addition to GenStat and R.
* Meta-analysis and the multiple testing problem.
* The Bayesian interpretation of mixed models.
Including numerous practical exercises with solutions, this bookprovides an ideal introduction to mixed modelling for final yearundergraduate students, postgraduate students and professionalresearchers. It will appeal to readers from a wide range ofscientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography.
Praise for the first edition:
„One of the main strengths of the text is the bridge itprovides between traditional analysis of variance and regressionmodels and the more recently developed class of mixed models... Eachchapter is well-motivated by at least one carefully chosenexample... demonstrating the broad applicability of mixed models inmany different disciplines... most readers will likely learnsomething new, and those previously unfamiliar with mixed modelswill obtain a solid foundation on thistopic.“--Kerrie Nelson University ofSouth Carolina, in American Statistician, 2007
