Monotonic and Semiparametric Regression for the Detection of Trends in Environmental Quality Data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Natural fluctuations in the state of the environment can long conceal or distort important trends in the human impact on our ecosystems. Accordingly, there is increasing interest in statistical normalisation techniques that can clarify the anthropogenic effects by removing meteorologically driven fluctuations and other natural variation in time series of environmental quality data. This thesis shows that semi- and nonparametric regression methods can provide effective tools for applying such normalisation to collected data. In particular, it is demonstrated how monotonic regression can be utilised in this context. A new numerical algorithm for this type of regression can accommodate two or more discrete or continuous explanatory variables, which enables simultaneous estimation of a monotonic temporal trend and correction for one or more covariates that have a monotonic relationship with the response variable under consideration. To illustrate the method, a case study of mercury levels in fish is presented, using body length and weight as covariates. Semiparametric regression techniques enable trend analyses in which a nonparametric representation of temporal trends is combined with parametrically modelled corrections for covariates. Here, it is described how such models can be employed to extract trends from data collected over several seasons, and this procedure is exemplified by discussing how temporal trends in the load of nutrients carried by the Elbe River can be detected while adjusting for water discharge and other factors. In addition, it is shown how semiparametric models can be used for joint normalisation of several time series of data.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. , 53 p.
Linköping Studies in Statistics, ISSN 1651-1700 ; 7Linköping Studies in Arts and Science, ISSN 0282-9800 ; 343
Normalisation, Monotonic, Semiparametric, Temporal trends, fluctuations, global, local
Matematisk statistik, Icke-parametriska metoder
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-5124ISBN: 91-85457-70-1OAI: oai:DiVA.org:liu-5124DiVA: diva2:21036
2005-12-16, BL32, B-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
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