A graphical method of exploring the mean structure in longitudinal data analysis?

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In a longitudinal study, individuals are observed over some period of time. The investigator wishes to model the responses over this time as a function of various covariates measured on these individuals. The times of measurement may be sparse and not coincident across individuals. When the covariate values are not extensively replicated, it is very difficult to propose a parametric model linking the response to the covariates because plots of the raw data are of little help. Although the response curve may only be observed at a few points, we consider the underlying curve y(t). We fit a regression model y(t) = xTβ(t) + ε(t) and use the coefficient functions β(t) to suggest a suitable parametric form. Estimates of y(t) are constructed by simple interpolation, and appropriate weighting is used in the regression. We demonstrate the method on simulated data to show its ability to recover the true structure and illustrate its application to some longitudinal data from the Panel Study of Income Dynamics.

Original languageEnglish
Pages (from-to)60-68
Number of pages9
JournalJournal of Computational and Graphical Statistics
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Jan 1999

Keywords

  • Curve estimation
  • Exploratory data analysis
  • Functional data analysis
  • Nonparametric regression
  • Repeated measures

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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