%0 Journal Article %1 millard1988nonparametric %A Millard, Steven P. %A Deverel, Steven J. %D 1988 %J Water Resources Research %K 62g05-nonparametric-inference-estimation 62n01-censored-data-models 62p12-applications-of-statistics-to-environmental-and-related-topics %N 12 %P 2087-2098 %R 10.1029/WR024I012P02087 %T Nonparametric statistical methods for comparing two sites based on data with multiple nondetect limits %U https://agupubs.onlinelibrary.wiley.com/doi/10.1029/WR024i012p02087 %V 24 %X As concern over the effects of trace amounts of pollutants has increased, so has the need for statistical methods that deal appropriately with data that include values reported as “less than” the detection limit. It has become increasingly common for water quality data to include censored values that reflect more than one detection limit for a single analyte. For such multiply censored data sets, standard statistical methods (for example, to compare analyte concentration in two areas) are not valid. In such cases, methods from the biostatistical field of survival analysis are applicable. Several common two-sample censored data rank tests are explained, and their behaviors are studied via a Monte Carlo simulation in which sample sizes and censoring mechanisms are varied under an assumed lognormal distribution. These tests are applied to shallow groundwater chemistry data from two sites in the San Joaquin Valley, California. The best overall test, in terms of maintained α level, is the normal scores test based on a permutation variance. In cases where the α level is maintained, however, the Peto-Prentice statistic based on an asymptotic variance performs as well or better.

@article{millard1988nonparametric, abstract = {As concern over the effects of trace amounts of pollutants has increased, so has the need for statistical methods that deal appropriately with data that include values reported as “less than” the detection limit. It has become increasingly common for water quality data to include censored values that reflect more than one detection limit for a single analyte. For such multiply censored data sets, standard statistical methods (for example, to compare analyte concentration in two areas) are not valid. In such cases, methods from the biostatistical field of survival analysis are applicable. Several common two-sample censored data rank tests are explained, and their behaviors are studied via a Monte Carlo simulation in which sample sizes and censoring mechanisms are varied under an assumed lognormal distribution. These tests are applied to shallow groundwater chemistry data from two sites in the San Joaquin Valley, California. The best overall test, in terms of maintained α level, is the normal scores test based on a permutation variance. In cases where the α level is maintained, however, the Peto-Prentice statistic based on an asymptotic variance performs as well or better.}, added-at = {2024-02-28T01:55:48.000+0100}, author = {Millard, Steven P. and Deverel, Steven J.}, biburl = {https://www.bibsonomy.org/bibtex/20bb95ab315304b1ecd62983ff49b93c0/gdmcbain}, doi = {10.1029/WR024I012P02087}, interhash = {d910a74ebf01bcb09be7d7ba7e740dd7}, intrahash = {0bb95ab315304b1ecd62983ff49b93c0}, journal = {Water Resources Research}, keywords = {62g05-nonparametric-inference-estimation 62n01-censored-data-models 62p12-applications-of-statistics-to-environmental-and-related-topics}, number = 12, pages = {2087-2098}, timestamp = {2024-02-28T01:56:25.000+0100}, title = {Nonparametric statistical methods for comparing two sites based on data with multiple nondetect limits}, url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/WR024i012p02087}, volume = 24, year = 1988 }