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Marginalized Transition Models and Likelihood Inference for Longitudinal Categorical Data
Author(s) -
Heagerty Patrick J.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00342.x
Subject(s) - marginal model , inference , econometrics , categorical variable , mathematics , quasi likelihood , generalized linear model , binary data , marginal likelihood , markov chain , longitudinal data , biometrics , statistics , parametric model , estimating equations , parametric statistics , regression analysis , computer science , maximum likelihood , binary number , count data , poisson distribution , data mining , artificial intelligence , arithmetic
Summary. Marginal generalized linear models are now frequently used for the analysis of longitudinal data. Semiparametric inference for marginal models was introduced by Liang and Zeger (1986, Biometrics 73 , 13–22). This article develops a general parametric class of serial dependence models that permits likelihood‐based marginal regression analysis of binary response data. The methods naturally extend the first‐order Markov models of Azzalini (1994, Biometrika 81 , 767–775) and prove computationally feasible for long series.