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- W2084206770 abstract "Abstract A survey of methods used by the Regional Water Authorities found that three techniques are commonly employed for percentile estimation: (1) assuming a parametric probability distribution; (2) using a non-parametric percentile estimation technique, and (3) statistically testing the fit of an assumed distribution. In this study, mathematical probability distribution fitting techniques were applied to 334 water quality datasets for a variety of rivers and effluents distributed throughout England and Wales. The results indicated that the “best” estimate of the 95 percentile from a dataset should be based upon probability distribution fitting. An untested assumption made about the underlying distribution of the data may produce an erroneous result in the subsequent water quality consent determination. The three parametric distributions selected (normal, lognormal and Pearson Type 3) could be fitted to half of the datasets studied. Results suggested that the Pearson Type 1 distribution may also be suitable for fitting to water quality data. The arbitrary use of non-parametric techniques may fail to make the most effective use of the information contained in the data." @default.
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- W2084206770 date "1987-05-01" @default.
- W2084206770 modified "2023-09-24" @default.
- W2084206770 title "Percentile estimation for water quality data" @default.
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- W2084206770 doi "https://doi.org/10.1016/0043-1354(87)90067-4" @default.
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