  Stochastic gradient algorithm is a key ingredient of many machine learning
methods, particularly appropriate for large-scale learning.However, a major
caveat of large data is their incompleteness.We propose an averaged stochastic
gradient algorithm handling missing values in linear models. This approach has
the merit to be free from the need of any data distribution modeling and to
account for heterogeneous missing proportion.In both streaming and
finite-sample settings, we prove that this algorithm achieves convergence rate
of $\mathcal{O}(\frac{1}{n})$ at the iteration $n$, the same as without missing
values. We show the convergence behavior and the relevance of the algorithm not
only on synthetic data but also on real data sets, including those collected
from medical register.
