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Variance reduction for trend analysis
Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
2002 (English)In: NORDSTAT 2002, Stockholm, Sweden, 2002Conference paper, Published paper (Other academic)
Abstract [en]

The concentrations of nutrients and other substances in a water body can be strongly influenced by random fluctuations in the mixing of waters of different origin. Hence, the water quality at given site can exhibit a large temporal variation that makes it difficult to extract anthropogenic signals from collected data. In this paper, we examine how the human impact on nutrient concentrations in such water bodies can be clarified by replacing conventional time series or geostatistical approaches by trend detection techniques in which we analyse the variation in nutrient concentrations with salinity and time. The general principles for the trend detection are illustrated with data from the Baltic Sea. The statistical significance of temporal changes in nutrient concentrations can be assessed by using parametric and nonparametric trend tests. In the recent past a nonparametric trend test with correction for covariates was proposed (Libiseller and Grimvall, 2002). This test, however, can best be applied if trends are monotone in time, which is not necessarily fulfilled for the original data. We therefore suggest that an overall trend test is computed as the weighted sum of trend test statistics computed for different salinity levels. By this means we receive a rather homogeneous time series in each subset, which considerably improves the power of the trend test. In the parametric approach we suggest a regression model, with Total Phosphorus concentration as the dependent variable and time (months) as the explaining variable. The residuals from this model output are most likely non-independent and non-normally distributed, and we will therefore apply bootstrap assessment of the estimated parameters.

Place, publisher, year, edition, pages
2002.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-29419Local ID: 14763OAI: oai:DiVA.org:liu-29419DiVA: diva2:250233
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2010-09-28

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Nordgaard, AndersLibiseller, Claudia

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