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Imputation and low-rank estimation with missing non at random data

This repository hosts the code to impute and estimate low-rank matrices containing Missing Not At Random (MNAR) values. Different matrix completion methods are considered, including a model-based estimation strategy by modelling the missing mechanism distribution. More details are given in [1].

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If you use this code in your project, please cite:

[1] Aude Sportisse, Claire Boyer, and Julie Josse. Imputation and low-rank estimation with missing non at random data. arXiv preprint arXiv:1812.11409, 2018.

Get the PDF of the paper here.

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