A statistical modeling framework for analyzing tree-indexed data : application to plant development on microscopic and macroscopic scales

Abstract : We address statistical models for tree-indexed data.Tree-indexed data can be seen as a generalization of path-indexed data since directed path graphs are directed tree graphs where there is at most one child per vertex.In the context of the Virtual Plants team, host team of this thesis, applications of interest focus on plant development and its modulation by environmental and genetic factors.We thus focus on plant developmental applications, both at the microscopic level with the study of the cell lineage in the biological tissue responsible for the plant growth, and at the macroscopic level with the mechanism of production of branches. The catalog of models available for tree-indexed data is far less important than the one available for path-indexed data.This thesis therefore aims at proposing a statistical modeling framework for studying patterns in tree-indexed data.To this end, two different classes of statistical models, Markov and change-point models, are investigated.
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Pierre Fernique. A statistical modeling framework for analyzing tree-indexed data : application to plant development on microscopic and macroscopic scales. Statistics [math.ST]. Université Montpellier II - Sciences et Techniques du Languedoc, 2014. English. ⟨NNT : 2014MON20064⟩. ⟨tel-01365814⟩

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