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.