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

Fu, Li Ya and Wang, You-Gan (2011) Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits. Environmental Science & Technology, 45 4: 1481-1489. doi:10.1021/es101304h


Author Fu, Li Ya
Wang, You-Gan
Title Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits
Journal name Environmental Science & Technology   Check publisher's open access policy
ISSN 0013-936X
1520-5851
Publication date 2011-02-01
Year available 2011
Sub-type Article (original research)
DOI 10.1021/es101304h
Volume 45
Issue 4
Start page 1481
End page 1489
Total pages 9
Place of publication Washington DC, United States
Publisher American Chemical Society
Collection year 2012
Language eng
Abstract 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 clearly demonstrates the advantages of the rank regression models.
Keyword S-language software
Failure time model
Statistical-analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Publication Date (Web): January 25, 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2012 Collection
 
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Created: Sun, 27 Mar 2011, 10:11:03 EST