Ricerc@Sapienza

Resampling methods are widely studied and increasingly employed in applied
research and practice. When dealing with complex sampling designs, common
resampling techniques require to adjust non-integer sampling weights in order to
construct the so called “pseudo-population” where to perform the actual resampling.
In particular, to lighten the computational burden, it is commonly suggested
to round resampling weights to the nearest integer. This practice, however, has
been empirically shown to be harmful under general designs. Here we develop

In this paper, a class of resampling techniques for finite populations under pps sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in: (i) constructing a pseudo-population" on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the "sampling design level". Theoretical justifications based on large sample theory are provided.