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Article Dans Une Revue Journal of the Royal Statistical Society: Series C Applied Statistics Année : 2021

Accounting for Missing Actors in Interaction Network Inference from Abundance Data

Résumé

Abstract Network inference aims at unravelling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies. In the context of count data, we introduce a mixture of Poisson log-normal distributions with tree-shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological data sets. The corresponding R package is available from github.com/Rmomal/nestor.

Dates et versions

hal-04495702 , version 1 (08-03-2024)

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Raphaëlle Momal, Stephane S. Robin, Christophe Ambroise. Accounting for Missing Actors in Interaction Network Inference from Abundance Data. Journal of the Royal Statistical Society: Series C Applied Statistics, 2021, 70 (5), pp.1230-1258. ⟨10.1111/rssc.12509⟩. ⟨hal-04495702⟩
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