An Introduction to (Generalized (Non)Linear Mixed Models

  • Geert Molenberghs
  • Geert Verbeke
Part of the Statistics for Social Science and Public Policy book series (SSBS)

Abstract

In applied sciences, one is often confronted with the collection of correlated data or otherwise hierarchical data. This generic term embraces a multitude of data structures, such as multivariate observations, clustered data, repeated measurements (called ‘repeated observations’ in this volume), longitudinal data, and spatially correlated data. In particular, studies are often designed to investigate changes in a specific parameter which is measured repeatedly over time in the participating persons. This is in contrast to cross-sectional studies where the response of interest is measured only once for each individual. Longitudinal studies are conceived for the investigation of such changes, together with the evolution of relevant covariates.

Keywords

Linear Mixed Model Generalize Linear Mixed Model American Statistical Association Exponential Family Royal Statistical Society 
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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© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Geert Molenberghs
  • Geert Verbeke

There are no affiliations available

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