Mathematical Sciences: Adaptive Sampling

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This research is to develop the theory and methodology of adaptive sampling designs. The proposed approach involves a combination of theoretical development to determine optimal adaptive designs and computer simulation to compare practical adaptive designs with non adaptive ones. Taking advantage of the conditional mean square error structure of the population, design-induced unbiasedness is proposed to provide robustness against possible departures from assumptions about populations. Simulated sampling of cluster point process realizations is proposed to examine one important class of adaptive designs. This research is in the subfield of experimental design, in the general area of statistics. The goal is to utilize the information in an experiment more efficiently by developing the theory of designing experiments so that experimental units may be sampled from a population according to rules that may depend sequentially on observations of previously selected units. Significant progress in this direction has the long term potential for revolutionizing the practice of scientific experimentation and providing for more efficient use of our national research and development funds.