Title: Repeated Out of Sample Fusion in Interval Estimation of Small Tail Probabilities in Food Safety

Speaker: Benjamin Kedem (UMD)

In food safety and bio-surveillance, in many cases, it is often desired to estimate the probability that a contaminant such as an insecticide or pesticide exceeds unsafe, very high thresholds. The probability or chance in question is then very small. To estimate such a probability we need information about large values. However, in many cases the data do not contain information about exceedingly large contamination levels, which ostensibly makes the problem impossible to solve. A solution is provided whereby more information about small tail probabilities is obtained by FUSING the real data with computer generated random data. The method provides short but reliable interval estimates from moderately large samples. An illustration is provided using exposure data of methylmercury, dichlorophenol, and trichlorophenol obtained from the National Health and Nutrition Examination Survey (NHANES).