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Optimal Sequential Surveillance for Finance, Public Health, and Other Areas


  • Frisén, Marianne

    () (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)


The aim of sequential surveillance is on-line detection of an important change in an underlying process as soon as possible after the change has occurred. Statistical methods suitable for surveillance differ from hypothesis testing methods. In addition, the criteria for optimality differ from those used in hypothesis testing. The need for sequential surveillance in industry, economics, medicine and for environmental purposes is described. Even though the methods have been developed under different scientific cultures, inferential similarities can be identified. Applications contain complexities such as autocorrelations, complex distributions, complex types of changes, and spatial as well as other multivariate settings. Approaches to handling these complexities are discussed. Expressing methods for surveillance through likelihood functions makes it possible to link the methods to various optimality criteria. This approach also facilitates the choice of an optimal surveillance method for each specific application and provides some directions for improving earlier suggested methods.

Suggested Citation

  • Frisén, Marianne, 2007. "Optimal Sequential Surveillance for Finance, Public Health, and Other Areas," Research Reports 2007:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
  • Handle: RePEc:hhs:gunsru:2007_002

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    References listed on IDEAS

    1. Max Petzold & Christian Sonesson & Eva Bergman & Helle Kieler, 2004. "Surveillance in Longitudinal Models: Detection of Intrauterine Growth Restriction," Biometrics, The International Biometric Society, vol. 60(4), pages 1025-1033, December.
    2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    3. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    4. Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 93-108.
    5. H. E. T. Holgersson, 2004. "Testing for Multivariate Autocorrelation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(4), pages 379-395.
    6. Sun, Kai & Basu, Asit P., 1995. "A characterization of a bivariate geometric distribution," Statistics & Probability Letters, Elsevier, vol. 23(4), pages 307-311, June.
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    Cited by:

    1. Verdier, Ghislain & Hilgert, Nadine & Vila, Jean-Pierre, 2008. "Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4161-4174, May.

    More about this item


    Change point; Likelihood ratio; Monitoring; Multivariate surveillance; Minimum expected delay; Online detection;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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