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Modelling discrete longitudinal data using acyclic probabilistic finite automata

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  • Ankinakatte, Smitha
  • Edwards, David

Abstract

Acyclic probabilistic finite automata (APFA) constitute a rich family of models for discrete longitudinal data. An APFA may be represented as a directed multigraph, and embodies a set of context-specific conditional independence relations that may be read off the graph. A model selection algorithm to minimize a penalized likelihood criterion such as AIC or BIC is described. This algorithm is compared to one implemented in Beagle, a widely used program for processing genomic data, both in terms of rate of convergence to the true model as the sample size increases, and a goodness-of-fit measure assessed using cross-validation. The comparisons are based on three data sets, two from molecular genetics and one from social science. The proposed algorithm performs at least as well as the algorithm in Beagle in both respects.

Suggested Citation

  • Ankinakatte, Smitha & Edwards, David, 2015. "Modelling discrete longitudinal data using acyclic probabilistic finite automata," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 40-52.
    • Handle: RePEc:eee:csdana:v:88:y:2015:i:c:p:40-52
    DOI: 10.1016/j.csda.2015.02.009
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    References listed on IDEAS

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    1. Edwards, David, 1992. "Linkage analysis using loglinear models," Computational Statistics & Data Analysis, Elsevier, vol. 13(3), pages 281-290, April.
    2. Gabadinho, Alexis & Ritschard, Gilbert & Müller, Nicolas S & Studer, Matthias, 2011. "Analyzing and Visualizing State Sequences in R with TraMineR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i04).
    3. Adrian Raftery & Simon Tavaré, 1994. "Estimation and Modelling Repeated Patterns in High Order Markov Chains with the Mixture Transition Distribution Model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 179-199, March.
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