Use of best linear unbiased prediction for hot spot identification in two-way compositing

Compositing of individual samples is a cost-effective method for estimating a population mean, but at the expense of losing information about the indi vidual sample values. The largest of these sample values (hotspot) is somet imes of particular interest. Sweep-out methods attempt to identify the hots pot and its value by quantifying a (hopefully, small) subset of individual values as well as the usual quantification of the composites. Sweep-out des ign is concerned with the sequential selection of individual samples for qu antification on the basis of all earlier quantifications (both composite an d individual). The design-goal is for the number of individual quantificati ons to be small (ideally, minimal). Previous sweep-out designs have applied to traditional (i.e., disjoint) compositing. This paper describes a sweep- out design suitable for two-way compositing. That is, the individual sample s are arranged in a rectangular array and a composite is formed from each r ow and also from each column. At each step, the design employs all availabl e measurements (composite and individual) to form the best linear unbiased predictions for the currently unquantified cells. The cell corresponding to the largest predicted value is chosen next for individual measurement. The procedure terminates when the hotspot has been identified with certainty.