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Normal-Theory Methods: Linear Mixed Models

Chapter
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Part of the Springer Texts in Statistics book series (STS)

Abstract

Chapters 3, 4, and 5 have considered the situation in which a normally distributed outcome variable is measured repeatedly from each subject or experimental unit. The analysis of such data must account for the dependence among a subject’s multiple measurements. “Classical” methodology is based either on multivariate normal models with general covariance structure (Chapters 3 and 4) or on univariate repeated measures ANOVA (Chapter 5). In practice, however, repeated measurements studies are characterized by:
  • variation among experimental units with respect to the number andtiming of observations;

  • missing data;

  • •|time-dependent covariates.

Keywords

Linear Mixed Model Bayesian Information Criterion Covariance Structure Covariance Model Cervical Dystonia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 2002

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