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Estimation of distributional parameters for censored trace level water quality data. 2. Verification and applications

Water Resources Research

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Abstract

Estimates of distributional parameters (mean, standard deviation, median, interquartile range) are often desired for data sets containing censored observations. Eight methods for estimating these parameters have been evaluated by R. J. Gilliom and D. R. Helsel (this issue) using Monte Carlo simulations. To verify those findings, the same methods are now applied to actual water quality data. The best method (lowest root-mean-squared error (rmse)) over all parameters, sample sizes, and censoring levels is log probability regression (LR), the method found best in the Monte Carlo simulations. Best methods for estimating moment or percentile parameters separately are also identical to the simulations. Reliability of these estimates can be expressed as confidence intervals using rmse and bias values taken from the simulation results. Finally, a new simulation study shows that best methods for estimating uncensored sample statistics from censored data sets are identical to those for estimating population parameters.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Estimation of distributional parameters for censored trace level water quality data. 2. Verification and applications
Series title:
Water Resources Research
Volume
22
Issue:
2
Year Published:
1986
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
147
Last page:
155
Number of Pages:
9