  In the regression problem, we consider the problem of estimating the variance
function by the means of aggregation methods. We focus on two particular
aggregation setting: Model Selection aggregation (MS) and Convex aggregation
(C) where the goal is to select the best candidate and to build the best convex
combination of candidates respectively among a collection of candidates. In
both cases, the construction of the estimator relies on a two-step procedure
and requires two independent samples. The first step exploits the first sample
to build the candidate estimators for the variance function by the
residual-based method and then the second dataset is used to perform the
aggregation step. We show the consistency of the proposed method with respect
to the L 2error both for MS and C aggregations. We evaluate the performance of
these two methods in the heteroscedastic model and illustrate their interest in
the regression problem with reject option.
