Flexible estimation of serial correlation in nonlinear mixed models

Jan Serroyen, Geert Molenberghs*, Marc Aerts, Ellen Vloeberghs, Peter Paul de Deyn, Geert Verbeke

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


In the conventional linear mixed-effects model, four structures can be distinguished: fixed effects, random effects, measurement error and serial correlation. The latter captures the phenomenon that the correlation structure within a subject depends on the time lag between two measurements. While the general linear mixed model is rather flexible, the need has arisen to further increase flexibility. In addition to work done in the area, we propose the use of spline-based modeling of the serial correlation function, so as to allow for additional flexibility. This approach is applied to data from a pre-clinical experiment in dementia which studied the eating and drinking behavior in mice.
Original languageEnglish
Pages (from-to)833-846
JournalJournal of Applied Statistics
Issue number5
Publication statusPublished - 2010


  • Alzheimer's disease
  • dementia
  • ordinary least squares
  • random effect

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