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Trend analysis of mercury in fish using nonparametric regression
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
2005 (English)Report (Other (popular science, discussion, etc.))
Abstract [en]

The International Council for the Exploration of the Sea (ICES) has longcompiled extensive data on contaminants in biota. We investigated how trendassessment of mercury in muscle tissue from fish (flounder and Atlantic cod)might be facilitated by using nonparametric regression to normalise observedlevels of this contaminant with respect to body length and weight. Specifically,we examined response surfaces and annual normalised means obtained byemploying purely additive models (AM), thin plate splines (TPS), andmonotonic regression (MR) to model mercury levels as functions of samplingyear and one or two covariates. Our analysis showed that TPS and MR modelscan be more satisfactory than purely additive models, because the formertechniques enable estimation of time-dependent relationships between themercury concentration and the covariates. However, the major obstacle fortrend assessment of the collected mercury data was a substantial interannualvariation that was related to factors other than body length and weight.Nevertheless, several time series of flounder data that started in the 1970s and1980s showed obvious downward trends, and these trends were particularly2strong in the Elbe estuary. When the analysis was limited to data collected after1990, an overall Mann-Kendall test for all sampling sites revealed astatistically significant downward trend for flounder, whereas it was notsignificant for cod.

Place, publisher, year, edition, pages
2005. no 7
Series
LIU-MAI-R, Department of Mathematics, Division of Statistics , 2005-07
Keyword [en]
additive models, thin plate splines, monotonic regression, trend assessment, normalisation, mercury, fish
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-13602OAI: oai:DiVA.org:liu-13602DiVA: diva2:21034
Available from: 2005-12-16 Created: 2005-12-16 Last updated: 2009-03-03
In thesis
1. Monotonic and Semiparametric Regression for the Detection of Trends in Environmental Quality Data
Open this publication in new window or tab >>Monotonic and Semiparametric Regression for the Detection of Trends in Environmental Quality Data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Natural fluctuations in the state of the environment can long conceal or distort important trends in the human impact on our ecosystems. Accordingly, there is increasing interest in statistical normalisation techniques that can clarify the anthropogenic effects by removing meteorologically driven fluctuations and other natural variation in time series of environmental quality data. This thesis shows that semi- and nonparametric regression methods can provide effective tools for applying such normalisation to collected data. In particular, it is demonstrated how monotonic regression can be utilised in this context. A new numerical algorithm for this type of regression can accommodate two or more discrete or continuous explanatory variables, which enables simultaneous estimation of a monotonic temporal trend and correction for one or more covariates that have a monotonic relationship with the response variable under consideration. To illustrate the method, a case study of mercury levels in fish is presented, using body length and weight as covariates. Semiparametric regression techniques enable trend analyses in which a nonparametric representation of temporal trends is combined with parametrically modelled corrections for covariates. Here, it is described how such models can be employed to extract trends from data collected over several seasons, and this procedure is exemplified by discussing how temporal trends in the load of nutrients carried by the Elbe River can be detected while adjusting for water discharge and other factors. In addition, it is shown how semiparametric models can be used for joint normalisation of several time series of data.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. 53 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 7Linköping Studies in Arts and Science, ISSN 0282-9800 ; 343
Keyword
Normalisation, Monotonic, Semiparametric, Temporal trends, fluctuations, global, local, Matematisk statistik, Icke-parametriska metoder
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-5124 (URN)91-85457-70-1 (ISBN)
Public defence
2005-12-16, BL32, B-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2005-12-16 Created: 2005-12-16 Last updated: 2014-09-05Bibliographically approved

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Hussian, MohamedGrimvall, Anders

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