Optimal Sequential Surveillance for Finance, Public Health, and Other Areas
AbstractThe 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.
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Bibliographic InfoPaper provided by Statistical Research Unit, Department of Economics, School of Business, Economics and Law, University of Gothenburg in its series Research Reports with number 2007:2.
Length: 36 pages
Date of creation: 01 Jan 2007
Date of revision:
Contact details of provider:
Postal: Statistical Research Unit, University of Gothenburg, Box 640, SE 40530 GÖTEBORG
Web page: http://www.statistics.gu.se/
Change point; Likelihood ratio; Monitoring; Multivariate surveillance; Minimum expected delay; Online detection;
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- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
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- 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, Elsevier, vol. 52(9), pages 4161-4174, May.
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