Treatment of Censored Data and Dependency among Sampling Distributions in Probabilistic Human Health Exposure Assessment
Probabilistic human health exposure assessment is commonly challenged by non-detected data in measurement datasets and dependencies among sampling distributions. Methods for dealing with non detected data, dependencies and associated uncertainty are introduced. An example case study was performed to demonstrate application of the methods and investigate how exposure estimates and associated uncertainty are affected by using different methods. The results show that improper handling of non detected data may cause bias in the mean or other statistics such as the 97.5 percentile, as well as their uncertainty. A maimum likelihood estimation (MLE)-bootstrap approach is suggested for dealing with non detected data. A modified two-stage Monte Carlo approach, in which sampling distributions obtained from bootstrap simulation are kept in pair-wise data formats, is able to capture complex dependencies among sampling distributions, and thus is recommended as a technique for propagating uncertainty.
Bootstrap Simulation Censored Data Monte Carlo Simulation Exposure Assessment, Uncertainty
Junyu Zheng H. Christopher Frey
College of Environmental Science and Engineering South China University of Technology, Guangzhou, Ch Department of Civil, Construction, and Environmental Engineering North Carolina State University, Ra
国际会议
The 1st International Conference on Risk Analysis and Crisis Response(首届风险分析与危机反应国际学术研讨会)
上海
英文
802-808
2007-09-25（万方平台首次上网日期，不代表论文的发表时间）