Self-modelling regression for longitudinal data with time-invariant covariates

Naomi S. Altman, Julio C. Villarreal

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

The authors propose the use of self-modelling regression to analyze longitudinal data with time invariant covariates. They model the population time curve with a penalized regression spline and use a linear mixed model for transformation of the time and response scales to fit the individual curves. Fitting is done by an iterative algorithm using off-the-shelf linear and nonlinear mixed model software. Their method is demonstrated in a simulation study and in the analysis of tree swallow nestling growth from an experiment that includes an experimentally controlled treatment, an observational covariate and multi-level sampling.

Original languageEnglish (US)
Pages (from-to)251-268
Number of pages18
JournalCanadian Journal of Statistics
Volume32
Issue number3
DOIs
StatePublished - Sep 2004

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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