Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Fu, Liya & Wang, You-Gan (2011) Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits. Environmental Science & Technology, 45(4), pp. 1481-1489.

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Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

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3 citations in Scopus
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4 citations in Web of Science®

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ID Code: 90444
Item Type: Journal Article
Refereed: Yes
Keywords: s-language software, failure time model, statistical-analysis
DOI: 10.1021/es101304h
ISSN: 0013-936X
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 03:14
Last Modified: 03 Dec 2015 05:38

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