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Likelihood analysis of joint marginal and conditional models for longitudinal categorical data
Author(s) -
Chen Baojiang,
Yi Grace Y.,
Cook Richard J.
Publication year - 2009
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.10014
Subject(s) - categorical variable , covariate , econometrics , consistency (knowledge bases) , marginal model , estimator , computer science , statistics , inference , statistical inference , random effects model , marginal likelihood , missing data , mathematics , maximum likelihood , artificial intelligence , regression analysis , medicine , meta analysis
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 at random. Simulation studies demonstrate 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. The Canadian Journal of Statistics © 2009 Statistical Society of Canada