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Article Dans Une Revue Statistics and Computing Année : 2022

Learning from missing data with the binary latent block model

Résumé

Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data. We propose a co-clustering model, based on the binary Latent Block Model, that aims to take advantage of this nonignorable nonresponses, also known as Missing Not At Random data (MNAR). A variational expectationmaximization algorithm is derived to perform inference and a model selection criterion is presented. We assess the proposed approach on a simulation study, before using our model on the voting records from the lower house of the French Parliament, where our analysis brings out relevant groups of MPs and texts, together with a sensible interpretation of the behavior of non-voters.
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Dates et versions

hal-03876850 , version 1 (28-11-2022)

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Gabriel Frisch, Jean-Benoist Leger, Yves Grandvalet. Learning from missing data with the binary latent block model. Statistics and Computing, 2022, 32 (9), pp.1-21. ⟨10.1007/s11222-021-10058-y⟩. ⟨hal-03876850⟩
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