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Performance of partial Mann–Kendall tests for trend detection in the presence of covariates
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
2002 (English)In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095X, Vol. 13, no 1, 71-84 p.Article in journal (Refereed) Published
Abstract [en]

Trend analyses of time series of environmental data are often carried out to assess the human impact on the environment under the influence of natural fluctuations in temperature, precipitation, and other factors that may affect the studied response variable. We examine the performance of partial Mann–Kendall (PMK) tests, i.e. trend tests in which the critical region is determined by the conditional distribution of one Mann-Kendall (MK) statistic for monotone trend, given a set of other MK statistics. In particular, we examine the impact of incorporating information regarding covariates in the Hirsch–Slack test for trends in serially correlated data collected over several seasons. Monte Carlo simulation of the performance of PMK tests demonstrates that the gain in power due to incorporation of relevant covariates can be large compared to the loss in power caused by irrelevant covariates. Furthermore, we have found that the asymptotic normality of the test statistics in such tests enables rapid and reliable determination of critical regions, unless the sample size is very small (n < 10) or the different MK statistics are very strongly correlated. A case study of water quality trends shows that PMK tests can detect and correct for rather complex relationships between river water quality and water discharge. The generic character of the PMK tests makes them particularly useful for scanning large sets of data for temporal trends.

Place, publisher, year, edition, pages
2002. Vol. 13, no 1, 71-84 p.
Keyword [en]
Covariates, Mann-Kendall tests, Natural fluctuations, Non-parametric tests, Trend detection, Water quality
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:liu:diva-47097DOI: 10.1002/env.507OAI: oai:DiVA.org:liu-47097DiVA: diva2:267993
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2012-12-14
In thesis
1. Considering meteorological variation in assessments of environmental quality trends
Open this publication in new window or tab >>Considering meteorological variation in assessments of environmental quality trends
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Time series of environmental data are collected to monitor the effectiveness of new emission reduction policies or to determine the general state of the environment. However, small gradual changes in such variables can easily be concealed by large fluctuations caused by prevailing weather conditions. Hence, there is a real need for procedures that facilitate separation of such natural variation from anthropogenic effects.

Taking meteorological or hydrological variables into consideration in a trend analysis can be done in several ways. The technique chosen to accomplish this objective depends on characteristics of the data set, for example the length of the time series and sampling frequencies, and the kind of relationships that exist between the response variable and the covariates. Two different approaches were examined in the studies underlying this thesis: multivariate non-parametric tests and parametric normalisation procedures. The non-parametric trend test proposed here was newly desinged, thus it was also necessary to conduct simulation studies to examine the performance of this method. By comparison, normalisation techniques have been used over the past few decades mainly to adjust for the impact of meteorological effects on air quality data. The choice of explanatory variables for such procedures was studied: first by examining variable selection procedures based on cross-validation, paying special attention to serially correlated response data; and secondly by considering variables derived from complex physics-based models as alternatives to measured variables. A number of other aspects that might influence the ability to detect trends were also explored, including level shifts due to instrument malfunctions.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 41 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 3
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22755 (URN)2073 (Local ID)91-7373-615-5 (ISBN)2073 (Archive number)2073 (OAI)
Public defence
2003-04-25, Sal Key 1, Keyhuset, Linköpings universitet, Linköping, 10:15 (Swedish)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14

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Libiseller, ClaudiaGrimvall, Anders

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