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Estimating artificial level shifts in the presence of smooth trends
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Department of Mathematics, National University of Laos, Vientiane, Laos.
2008 (English)In: Environmental Monitoring and Assessment, ISSN 0167-6369Article in journal (Other academic) Submitted
Abstract [en]

Changes in observational data over time can be severely distorted by errors in measurements, sampling, or reporting. Here, we show how smooth trends in vector time series can be separated from one or two abrupt level shifts that occur simultaneously in all coordinates. Trends are modelled nonparametrically, whereas abrupt changes and the impact of covariates are modelled parametrically. The model is estimated using a backfitting algorithm in which estimation of smooth trends is alternated with estimation of regression coefficients for covariates and assessment of sudden level shifts. The proposed method is adaptive in the sense that the degree of smoothing over time and across coordinates is controlled by a roughness penalty and cross-validation procedure that automatically identifies the interdependence of the analysed data. Furthermore, it uses a resampling technique that can accommodate correlated error terms in the assessment of the uncertainty of both smooth trends and discontinuities. The method is applied to water quality data from Swedish national monitoring programmes to illustrate how known discontinuities can be quantified and how previously unrecognized discontinuities can be detected.

Place, publisher, year, edition, pages
2008.
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:liu:diva-52245OAI: oai:DiVA.org:liu-52245DiVA: diva2:280807
Available from: 2009-12-11 Created: 2009-12-11 Last updated: 2011-05-20Bibliographically approved
In thesis
1. Roadmap for trend detection and assessment of data quality
Open this publication in new window or tab >>Roadmap for trend detection and assessment of data quality
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Regular measurements of the state of the environment constitute a cornerstone of environmental management. Without the support of long time series of reliable data, we would know much less about changes that occur in the environment and their causes. The present research aimed to explore how improved techniques for data analysis can help reveal flawed data and extract more information from environmental monitoring programmes. Based on our results, we propose that the organization of such monitoring should be transformed from a system for measuring and collecting data to an information system where resources have been reallocated to data analysis. More specifically, this thesis reports improved methods for joint analysis of trends in multiple time series and detection of artificial level shifts in the presence of smooth trends. Furthermore, special consideration is given to methods that automatically detect and adapt to the interdependence of the collected data. The current work resulted in a roadmap describing the process of proceeding from a set of observed concentrations to arrive at conclusions about the quality of the data and existence of trends therein. Improvements in existing software accompanied the development of new statistical procedures.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2008. 81 + papers 1-5 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 10Linköping Studies in Arts and Science, ISSN 0282-9800 ; 454
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-43109 (URN)71719 (Local ID)978-91-7393-792-4 (ISBN)71719 (Archive number)71719 (OAI)
Public defence
2008-10-10, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Supervisors
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-09-25Bibliographically approved

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Wahlin, KarlGrimvall, Anders

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
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  • sv-SE
  • Other locale
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Output format
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