Optimal Sequential Surveillance for Finance, Public Health, and Other Areas
Abstract: 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
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.
Keywords: Change point; Likelihood ratio; Monitoring; Multivariate surveillance; Minimum expected delay; Online detection; (follow links to similar papers)
JEL-Codes: C10; (follow links to similar papers)
36 pages, January 1, 2007
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