Learning from missing data with the Latent Block Model

• URL: http://arxiv.org/abs/2010.12222v1
• Date: Fri, 23 Oct 2020 08:11:43 GMT
• Title: Learning from missing data with the Latent Block Model
• Authors: Gabriel Frisch (Heudiasyc), Jean-Benoist L\'eger (Heudiasyc), Yves Grandvalet (Heudiasyc)
• Abstract summary: We propose a co-clustering model, based on the Latent Block Model, that aims to take advantage of Missing Not At Random data. A variational expectation-maximization algorithm is derived to perform inference and a model selection criterion is presented.
• Score: 0.5735035463793007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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