Antedependence models for nonstationary categorical longitudinal data with ignorable missingness: likelihood-based inference.

ABSTRACT: Time index-ordered random variables are said to be antedependent (AD) of order (p1 ,p2 , … ,pn ) if the kth variable, conditioned on the pk immediately preceding variables, is independent of all further preceding variables. Inferential methods associated with AD models are well developed for continuous (primarily normal) longitudinal data, but not for categorical longitudinal data. In this article, we develop likelihood-based inferential procedures for unstructured AD models for categorical longitudinal data. Specifically, we derive maximum likelihood estimators (MLEs) of model parameters; penalized likelihood criteria and likelihood ratio tests for determining the order of antedependence; and likelihood ratio tests for homogeneity across groups, time invariance of transition probabilities

SUBMITTER: Xie Y 

PROVIDER: S-EPMC3885186 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature