Estimation of Distributional Parameters for Censored Trace Level Water Quality Data: 2. Verification and Applications
Estimation of Distributional Parameters for Censored Trace Level Water Quality Data: 2. Verification and Applications
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截尾痕量水质数据分布参数的估计：2.验证和应用
DOI：
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发表时间：
1986
期刊：
影响因子：
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通讯作者：
R. Gilliom
中科院分区：
文献类型：
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作者：
D. Helsel;R. Gilliom
对于包含删失观测值的数据集，通常需要估计分布参数（平均值、标准差、中位数、四分位距）。用R. J. Gilliom和D. R. Helsel（本期）使用蒙特卡罗模拟。为了验证这些发现，现在将相同的方法应用于实际水质数据。在所有参数、样本量和删失水平上，最佳方法（最低均方根误差（rmse））是对数概率回归（LR），该方法在蒙特卡洛模拟中表现最佳。分别估计矩或百分位数参数的最佳方法也与模拟相同。这些估计值的可靠性可以用从模拟结果中得到的rmse和偏差值表示为置信区间。最后，一个新的模拟研究表明，最好的方法来估计未经审查的样本统计量的删失数据集是相同的估计人口参数。因此，这项研究和同伴的研究Gilliom和Helsel形成的基础，使人口参数或样本统计数据的删失的水质数据的最佳可能的估计，并评估其可靠性。
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. Thus this study and the companion study by Gilliom and Helsel form the basis for making the best possible estimates of either population parameters or sample statistics from censored water quality data, and for assessments of their reliability.