liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Variance reduction for trend analysis
Linköpings universitet, Filosofiska fakulteten. Linköpings universitet, Matematiska institutionen, Statistik.
Linköpings universitet, Filosofiska fakulteten. Linköpings universitet, Matematiska institutionen, Statistik.
2002 (Engelska)Ingår i: NORDSTAT 2002, Stockholm, Sweden, 2002Konferensbidrag, Publicerat paper (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
2002.
Nationell ämneskategori
Matematik
Identifikatorer
URN: urn:nbn:se:liu:diva-29419Lokalt ID: 14763OAI: oai:DiVA.org:liu-29419DiVA, id: diva2:250233
Tillgänglig från: 2009-10-09 Skapad: 2009-10-09 Senast uppdaterad: 2010-09-28

Open Access i DiVA

Fulltext saknas i DiVA

Personposter BETA

Nordgaard, AndersLibiseller, Claudia

Sök vidare i DiVA

Av författaren/redaktören
Nordgaard, AndersLibiseller, Claudia
Av organisationen
Filosofiska fakultetenStatistik
Matematik

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 39 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf