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Learning from missing data with the Latent Block Model

Abstract : 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 Latent Block Model, that aims to take advantage of this nonignorable nonresponses, also known as Missing Not At Random data (MNAR). A variational expectation-maximization 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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Contributor : Gabriel Frisch <>
Submitted on : Wednesday, October 21, 2020 - 12:01:56 PM
Last modification on : Saturday, December 19, 2020 - 10:26:01 AM


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  • HAL Id : hal-02973814, version 1
  • ARXIV : 2010.12222



Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet. Learning from missing data with the Latent Block Model. 2020. ⟨hal-02973814⟩



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