Declustering of Clustered Preferential Sampling for Histogram and Semivariogram Inference

Abstract Measurements of attributes obtained more as a consequence of business ventures than sampling design frequently result in samplings that are preferential both in location and value, typically in the form of clusters along the pay. Preferential sampling requires preprocessing for the purpose of properly inferring characteristics of the parent population, such as the cumulative distribution and the semivariogram. Consideration of the distance to the nearest neighbor allows preparation of resampled sets that produce comparable results to those from previously proposed methods. A clustered sampling of size 140, taken from an exhaustive sampling, is employed to illustrate this approach.