  We define a general V-fold cross-validation type method based on robust
tests, which is an extension of the hold-out defined by Birg{\'e} [7, Section
9]. We give some theoretical results showing that, under some weak assumptions
on the considered statistical procedures, our selected estimator satisfies an
oracle type inequality. We also introduce a fast algorithm that implements our
method. Moreover we show in our simulations that this V-fold performs generally
well for estimating a density for different sample sizes, and can handle
well-known problems, such as binwidth selection for histograms or bandwidth
selection for kernels. We finally provide a comparison with other classical
V-fold methods and study empirically the influence of the value of V on the
risk.
