Spatially balanced adaptive web sampling

The paper deals with sampling from a finite population that is distributed over space and has an highly uneven spatial distribution. It suggests a sampling design that allocates a portion of the sample units well-spread over the population and selects sequentially the remaining units in sub-areas that appear of more interest according to the study variable values observed during the survey. In order to estimate the population mean while using this sampling design, a computational intense estimator, obtained via the Rao-Blackwell approach, is proposed and a resampling method that makes the inference computationally feasible is used. The whole sampling strategy is evaluated through several Monte Carlo experiments.