Estimating percentiles requires specialized methods from "summary" of Statistics for Censored Environmental Data Using Minitab and R by Dennis R. Helsel
When dealing with censored environmental data, it is important to understand the challenges presented in estimating percentiles. Traditional methods for estimating percentiles may not be suitable for censored data, as they do not account for the presence of non-detects or values below the detection limit. In these cases, specialized methods are required to accurately estimate percentiles. One common approach for estimating percentiles in censored data is the Kaplan-Meier method, which takes into account the presence of non-detects and censored values. This method calculates the probability of exceeding a certain threshold based on the observed data and the known detection limits. By considering the censored values, the Kaplan-Meier method provides a more accurate estimate of percentiles in censored data sets. Another specialized method for estimating percentiles in censored data is the Turnbull method, which uses a non-parametric approach to estimate percentiles. This method accounts for the censored values by fitting a piecewise constant hazard function to the data and estimating the percentiles based on this model. The Turnbull method is particularly useful when dealing with highly censored data sets, as it can provide reliable estimates of percentiles in these cases. In addition to these specialized methods, there are other techniques that can be used to estimate percentiles in censored data, such as maximum likelihood estimation and regression methods. These methods take into account the presence of censored values and provide more accurate estimates of percentiles compared to traditional methods.- Estimating percentiles in censored environmental data requires specialized methods that can account for the unique challenges posed by non-detects and censored values. By using these specialized methods, researchers can obtain more accurate and reliable estimates of percentiles in censored data sets, leading to better decision-making and risk assessment in environmental studies.
