Some statistical learning problems in the presence of incomplete data.

Summary Most statistical methods are not natively designed to work on incomplete data. The study of incomplete data is not new and many results have been established to overcome the incompleteness before the statistical study. On the other hand, deep learning methods are generally applied to unstructured data such as images, text or audio, but few works are interested in developing this type of approach on tabular data, and even less on incomplete data. This thesis focuses on the use of machine learning algorithms applied to tabular data, in the presence of incompleteness and in an insurance framework. Through the contributions gathered in this paper, we propose different ways to model complex phenomena in the presence of incompleteness patterns. We show that the proposed approaches give better results than the state of the art.