Abstract
In the classical problem of quickest change detection, a decision maker observes a sequence of random variables. At some point in time, the distribution of the random variables changes abruptly. The objective is to detect this change in distribution with minimum possible delay, subject to a constraint on the false alarm rate. In many applications of quickest change detection, e.g., where the changes are infrequent, it is of interest to control the cost of observations or the cost of data acquired before the change point. To this end, in this survey paper, data-efficient versions of the classical quickest change detection problems are studied, and a summary of existing results on these problems is provided. Some extensions to distributed sensor networks are also discussed. DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7790
Highlights
The problem of detecting an abrupt change in the statistical properties of a phenomenon under observation is encountered in many applications
In DE-quickest change detection (QCD), the objective of the decision maker is to minimize a metric on the detection delay, subject to constraints on a metric on the false alarm and a metric on the cost of observations used before the change point
Data-Efficient Minimax Quickest Change Detection we discuss the extension of the minimax formulations of Lorden and Pollak for data-efficient quickest change detection (DE-QCD) we studied in Banerjee and Veeravalli (2013a)
Summary
The problem of detecting an abrupt change in the statistical properties of a phenomenon under observation is encountered in many applications. In the classical problem formulations, there is a penalty on acquiring data after the change through the metric on delay, there is no penalty on the cost of observations taken before the change point. This motivates the study of a data-efficient version of the classical QCD problem. In DE-QCD, the objective of the decision maker is to minimize a metric on the detection delay, subject to constraints on a metric on the false alarm and a metric on the cost of observations used before the change point.
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