title: Quantification of variability and uncertainty for censored data sets and application to air toxic emission factors.
abstract: Many environmental data sets, such as for air toxic emission factors, contain several values reported only as below detection limit. Such data sets are referred to as "censored." Typical approaches to dealing with the censored data sets include replacing censored values with arbitrary values of zero, one-half of the detection limit, or the detection limit. Here, an approach to quantification of the variability and uncertainty of censored data sets is demonstrated. Empirical bootstrap simulation is used to simulate censored bootstrap samples from the original data. Maximum likelihood estimation (MLE) is used to fit parametric probability distributions to each bootstrap sample, thereby specifying alternative estimates of the unknown population distribution of the censored data sets. Sampling distributions for uncertainty in statistics such as the mean, median, and percentile are calculated. The robustness of the method was tested by application to different degrees of censoring, sample sizes, coefficients of variation, and numbers of detection limits. Lognormal, gamma, and Weibull distributions were evaluated. The reliability of using this method to estimate the mean is evaluated by averaging the best estimated means of 20 cases for small sample size of 20. The confidence intervals for distribution percentiles estimated with bootstrap/MLE method compared favorably to results obtained with the nonparametric Kaplan-Meier method. The bootstrap/MLE method is illustrated via an application to an empirical air toxic emission factor data set.
authors.affiliation: Department of Civil, Construction, and Environmental Engineering, North Carolina State University Raleigh, NC, USA.
authors.forename: Yuchao
authors.initials: Y
authors.lastname: Zhao
authors.forename: H Christopher
authors.initials: HC
authors.lastname: Frey
mesh_terms: D000388:Air; D000397:Air Pollution; D016001:Confidence Intervals; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D018570:Risk Assessment; D013223:Statistics as Topic; D018709:Statistics, Nonparametric; D035501:Uncertainty