Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published October 31, 2008 | public
Journal Article

Sequential Change-Point Detection Procedures That are Nearly Optimal and Computationally Simple


Sequential schemes for detecting a change in distribution often require that all of the observations be stored in memory. Lai (1995, Journal of Royal Statistical Society, Series B 57 : 613 – 658) proposed a class of detection schemes that enable one to retain a finite window of the most recent observations, yet promise first-order optimality. The asymptotics are such that the window size is asymptotically unbounded. We argue that what's of computational importance isn't having a finite window of observations, but rather making do with a finite number of registers. We illustrate in the context of detecting a change in the parameter of an exponential family that one can achieve eventually even second-order asymptotic optimality through using only three registers for storing information of the past. We propose a very simple procedure, and show by simulation that it is highly efficient for typical applications.

Additional Information

© 2008 Taylor & Francis Group, LLC. Received 07 Jan 2008, Accepted 25 Jul 2008, Published online: 31 Oct 2008. Moshe Pollak's research was supported by a grant from the Israel Science Foundation.

Additional details

August 22, 2023
October 18, 2023