published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Statistics / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

The sampling dynamics of the pink bollworm (Pectinophora gossypiella (Saunders)) were studied during the cotton growing seasons of 1976, 1977, and 1978. Different sampling methods were employed each year to compare sampling intensity and to compare whole plant samples with susceptible boll samples. Analysis of variance data indicated that significant differences in mean boll infestation levels occurred between small areas of a field (ca. 1 acre), but that these differences were masked when larger units (ca. 10 acres) of a field were used as the sample area. From these data it was found that samples yielded the most accurate pink bollworm infestation estimate when taken from as large an area of the field as possible. Data indicated that accuracy improved only 2 to 3 percent when sample size increased from 60 bolls to 100 bolls. Bolls taken from whole plant samples were as accurate as susceptible boll samples if there was an equal number of bolls in each sample. Sample accuracy was nearly stable for fields 20 to 40 acres in size. A sequential sampling plan for pink bollworm was developed using the binomial distribution. Decision levels were established using the 6 and 12 percent boll infestation levels with α and β levels of 0.2. Approximately 50 percent of the sampling time can be saved over conventional 100 boll samples with very little loss of accuracy for a spray-no spray decision based upon an economic threshold value of 15 percent pink bollworm infestation.

Research Project Technical Completion Report, Project No. G828-24, Prepared for the U.S. Department of the Interior, September 1984. / / With the growing problem of ground water contamination by volatile organic substances, drinking water sampling programs are being initiated throughout the United States. A need was recognized for a manual on well water sampling that would bridge the gap between highly technical documents and over -simplified reviews. A primer was therefore developed on establishing and implementing a sampling program. Current information was collected by means of both library research and extensive contact with public agency employees and practicing hydrologists. Portions of a sampling program that required explanation included selection of priority wells, proper sampling at the well head, sample preservation and shipment, chain of custody procedures, laboratory selection, quality assurance and data evaluation. Explanation of these steps is intended to help ensure the legal defensibility of any collected data.

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In this paper, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data.