A resampling technique for estimating the power of non-parametric trend tests
2004 (English)In: COMPSTAT 2004, Prague, Czech Republic, 2004Conference paper (Other academic)
The power of Mann-Kendall tests and other non-parametric trend tests is normally estimated by performing Monte-Carlo simulations in which artificial data are generated according to simple parametric models. Here we introduce a resampling technique for power assessments that can be fully automated and accommodate almost any variation incollected time series data. A rank regression model is employed to extract error terms representing irregular variation in data that have been gathered over several seasons and may contain a non-linear trend. Thereafter, an autoregressive bootstrap method is used to generate new time series of error terms for power simulations. These innovations are combined with trend and seasonal components from the fitted rank regression model, and the trend function can be resampled. We also describe a study of water quality data from two Swedish rivers to illustrate how our method can provide site- and variable-specific information about the power of the Hirsch and Slack test for monotonic trends. In particular, we show how our technique can clarify the impact of sampling frequency on the power of this type of trend test.
Place, publisher, year, edition, pages
power function, resampling, non-parametric trend test, bootstrap
IdentifiersURN: urn:nbn:se:liu:diva-29418Local ID: 14762OAI: oai:DiVA.org:liu-29418DiVA: diva2:250232