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Power Simulations using Data generated by Process-based Deterministic Models
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 .
2004 (English)In: COMPSTAT 2004,2004, 2004Conference paper (Other academic)
Abstract [en]

Power analysis is an integral part of statistical hypothesis testing, and, when neither exactpower computations nor reasonable approximations are feasible, Monte Carlo simulations providea viable alternative. However, generating data for such simulations is often an intricate task, especially when the hypothesis testing is based on non-normal multivariate data withcomplex dependencies. Here, we show how process-based deterministic models can be employed to generate data with adequate statistical dependencies for realistic power simulations. In particular, we usedthe Integrated Nitrogen in Catchments INCA model to produce bivariate time series ofnitrogen concentration and water discharge data that included plausible temporal trends andrealistic cross-correlations, seasonal patterns, and memory effects. The random variation in thegenerated data was achieved by running the INCA model with various sets of weather data that were obtained by block resampling from a given time series of observed air temperatureand precipitation data. The assortment of temporal trends was created by altering the anthropogenic input of nitrogen to the catchment under consideration.Two tests for temporal trends in nitrogen concentration were compared: i a partial Mann-Kendall test in which water discharge was treated as a covariate; ii a two-stage procedurein which we first used a semi-parametric regression technique to remove the impact of natural fluctuations in water discharge, and we subsequently applied an ordinary Mann-Kendall testto the obtained residuals. Our simulations demonstrated that the two tests had comparablepower, but also that they involved empirical significance levels that were much higher than the nominal levels, possibly due to substantial serial dependence in the data.

Place, publisher, year, edition, pages
Keyword [en]
artificial data, environmental trends, partial Mann-Kendall test, semi-parametric regression, normalisation
National Category
URN: urn:nbn:se:liu:diva-22689Local ID: 1984OAI: diva2:243002
Available from: 2009-10-07 Created: 2009-10-07

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Libiseller, ClaudiaGrimvall, Anders
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