Novel approach to predicting the release probability when applying the MARSSIM statistical test to a survey unit with a specific residual radioactivity distribution based on Monte Carlo simulation
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For investigating whether the MARSSIM nonparametric test has sufficient statistical power when a sitehas a specific contamination distribution before conducting a final status survey (FSS), a novel approachwas proposed to predict the release probability of the site. Five distributions were assumed: lognormaldistribution, normal distribution, maximum extreme value distribution, minimum extreme value distribution, and uniform distribution. Hypothetical radioactivity populations were generated for eachdistribution, and Sign tests were performed to predict the release probabilities after extracting samplesusing Monte Carlo simulations. The designed Type I error (0.01, 0.05, and 0.1) was always satisfied for alldistributions, while the designed Type II error (0.01, 0.05, and 0.1) was not always met for the uniform,maximum extreme value, and lognormal distributions. Through detailed analyses for lognormal andnormal distributions which are often found for contaminants in actual environmental or soil samples, itwas found that a greater statistical power was obtained from survey units with normal distribution thanwith lognormal distribution. This study is expected to contribute to achieving the designed decision errorwhen the contamination distribution of a survey unit is identified, by predicting whether the survey unitpasses the statistical test before undertaking the FSS according to MARSSIM
Abstract
For investigating whether the MARSSIM nonparametric test has sufficient statistical power when a sitehas a specific contamination distribution before conducting a final status survey (FSS), a novel approachwas proposed to predict the release probability of the site. Five distributions were assumed: lognormaldistribution, normal distribution, maximum extreme value distribution, minimum extreme value distribution, and uniform distribution. Hypothetical radioactivity populations were generated for eachdistribution, and Sign tests were performed to predict the release probabilities after extracting samplesusing Monte Carlo simulations. The designed Type I error (0.01, 0.05, and 0.1) was always satisfied for alldistributions, while the designed Type II error (0.01, 0.05, and 0.1) was not always met for the uniform,maximum extreme value, and lognormal distributions. Through detailed analyses for lognormal andnormal distributions which are often found for contaminants in actual environmental or soil samples, itwas found that a greater statistical power was obtained from survey units with normal distribution thanwith lognormal distribution. This study is expected to contribute to achieving the designed decision errorwhen the contamination distribution of a survey unit is identified, by predicting whether the survey unitpasses the statistical test before undertaking the FSS according to MARSSIM
참고문헌 (21)
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