Context-specific graphical models for discret longitudinal data

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  • David Edwards, Danmark
  • Smitha Anantharama Ankinakatte, Department of Statistics, Mangalore University, India, Danmark
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.
OriginalsprogEngelsk
TidsskriftStatistical Modelling
Vol/bind15
Nummer4
Sider (fra-til)301-325
Antal sider24
ISSN1471-082X
DOI
StatusUdgivet - aug. 2015

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