  Standard approaches for variable selection in linear models are not tailored
to deal properly with high-dimensional and incomplete data. Currently, methods
dedicated to high-dimensional data handle missing values by ad-hoc strategies,
like complete case analysis or single imputation, while methods dedicated to
missing values, mainly based on multiple imputation, do not discuss the
imputation method to use with high-dimensional data. Consequently, both
approaches appear to be limited for many modern applications.
  With inspiration from ensemble methods, a new variable selection method is
proposed. It extends classical variable selection methods in the case of
high-dimensional data with or without missing data. Theoretical properties are
studied and the practical interest is demonstrated through a simulation study,
as well as through an application to models specification in sequential
multiple imputation.
  In the low dimensional case, the procedure improves the control of the error
risks, especially type I error, even without missing values for stepwise, lasso
or knockoff methods. With missing values, the method performs better than
reference selection methods based on multiple imputation. Similar performances
are obtained in the high-dimensional case with or without missing values.
