Optimal Sequential Surveillance for Finance, Public Health, and Other Areas
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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||01 Jan 2007|
|Contact details of provider:|| Postal: Statistical Research Unit, University of Gothenburg, Box 640, SE 40530 GÖTEBORG|
Web page: http://www.statistics.gu.se/
When requesting a correction, please mention this item's handle: RePEc:hhs:gunsru:2007_002. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Linus Schiöler)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.