  This text is the rejoinder following the discussion of a survey paper about
minimal penalties and the slope heuristics (Arlot, 2019. Minimal penalties and
the slope heuristics: a survey. Journal de la SFDS). While commenting on the
remarks made by the discussants, it provides two new results about the slope
heuristics for model selection among a collection of projection estimators in
least-squares fixed-design regression. First, we prove that the slope
heuristics works even when all models are significantly biased. Second, when
the noise is Gaussian with a general dependence structure, we compute
expectations of key quantities, showing that the slope heuristics certainly is
valid in this setting also.
