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Abstract
Change-point detection methods are proposed for the case of temporary failures, or transient changes, when an unexpected disorder is ultimately followed by a re-adjustment and return to the initial state. A base distribution of the ‘in-control’ state changes to an ‘out-of-control’ distribution for unknown periods of time. Likelihood based sequential and retrospective tools are proposed for the detection and estimation of each pair of change-points. The accuracy of the obtained change-point estimates is assessed. Proposed methods offer simultaneous control of the familywise false alarm and false re-adjustment rates at the pre-chosen levels.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Research of M. Baron is supported by the NSF [grant number 1737960] and the Defense Advanced Research Projects Agency (DARPA) [grant number HR0011-18-C-0051]. Research of S. Malov is supported by the Russian Science Foundation (RSF) [grant number 20-14-00072].