Computational Method for Ranking Task-specific Exposures Using Multi-task Time-weighted Average Samples

摘要

A method is presented for ranking task-specific exposures using time-weighted average samples collected during the performance of multiple tasks. The task ranking can be used for purposes such as prioritizing further assessment or control. No a priori estimates of the individual task concentration distributions are required. Sample concentrations and task-specific concentrations are assumed to be log-normally distributed, and each known sample concentration is modeled as the geometric time-weighted average of the unknown task concentrations.