Likelihood analysis of joint marginal and conditional models for longitudinal categorical data

Baojiang Chen, Grace Y. Yi, Richard J. Cook

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The authors develop a Markov model for the analysis of longitudinal categorical data which facilitates modelling both marginal and conditional structures. A likelihood formulation is employed for inference, so the resulting estimators enjoy the optimal properties such as efficiency and consistency, and remain consistent when data are missing a trandom. Simulation studies demon strate that the proposed method performs well under a variety of situations. Application to data from a smoking prevention study illustrates the utility of the model and interpretation of covariate effects.

Original languageEnglish (US)
Pages (from-to)182-205
Number of pages24
JournalCanadian Journal of Statistics
Volume37
Issue number2
DOIs
StatePublished - Jun 2009

Keywords

  • Categorical data
  • Conditional model
  • Longitudinal data
  • Marginal model
  • Maximum likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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