Context-specific graphical models for discrete longitudinal data. (August 2015)

• Record Type: Journal Article
• Authors: Edwards, David; Ankinakatte, Smitha
• Is Part Of: Statistical modelling. Volume 15:Number 4(2015)
• Page Start: 301
• Page End: 325
• Publication Date: 2015-08
• Subjects: acyclic probabilistic finite automata -- Graphical model context-specific -- state merging -- conditional independence -- Markov -- chain event graph; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X14551248
• Languages: English
• ISSNs: 1471-082X

Abstract

Ron et al. (1998) introduced a rich family of models for discrete longitudinal data called acyclic probabilistic finite automata. These may be represented as directed graphs that embody context-specific conditional independence relations. Here, the approach is developed from a statistical perspective. It is shown here that likelihood ratio tests may be constructed using standard contingency table methods, a model selection procedure that minimizes a penalized likelihood criterion is described, and a way to extend the models to incorporate covariates is proposed. The methods are applied to a small-scale dataset. Finally, it is shown that the models generalize certain subclasses of conventional undirected and directed graphical models.