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Estimating finite mixture of continuous trees using penalized mutual information

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  • Atefeh Khalili
  • Farzad Eskandari

Abstract

In this paper we introduce continuous tree mixture model that is the mixture of undirected graphical models with tree structured graphs and is considered as multivariate analysis with a non parametric approach. We estimate its parameters, the component edge sets and mixture proportions through regularized maximum likalihood procedure. Our new algorithm, which uses expectation maximization algorithm and the modified version of Kruskal algorithm, simultaneosly estimates and prunes the mixture component trees. Simulation studies indicate this method performs better than the alternative Gaussian graphical mixture model. The proposed method is also applied to water-level data set and is compared with the results of Gaussian mixture model.

Suggested Citation

  • Atefeh Khalili & Farzad Eskandari, 2020. "Estimating finite mixture of continuous trees using penalized mutual information," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(20), pages 4974-4987, October.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:20:p:4974-4987
    DOI: 10.1080/03610926.2019.1609519
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