Sequential Change-Point Detection When the Pre- and Post-Change Parameters are Unknown

Tze Leung Lai; Haipeng Xing

doi:10.1080/07474941003741078

Sequential Change-Point Detection When the Pre- and Post-Change Parameters are Unknown

Tze Leung Lai
, Haipeng Xing

Applied Mathematics and Statistics

Stanford University

Research output: Contribution to journal › Article › peer-review

52 Scopus citations

Abstract

We describe asymptotically optimal Bayesian and frequentist solutions to the problem of sequential change-point detection in multiparameter exponential families when the pre- and post-change parameters are unknown. In this connection we also address certain issues recently raised by Mei (2008) concerning performance criteria for detection rules in this setting.

Original language	English
Pages (from-to)	162-175
Number of pages	14
Journal	Sequential Analysis
Volume	29
Issue number	2
DOIs	https://doi.org/10.1080/07474941003741078
State	Published - Apr 2010

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abstract = "We describe asymptotically optimal Bayesian and frequentist solutions to the problem of sequential change-point detection in multiparameter exponential families when the pre- and post-change parameters are unknown. In this connection we also address certain issues recently raised by Mei (2008) concerning performance criteria for detection rules in this setting.",

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AB - We describe asymptotically optimal Bayesian and frequentist solutions to the problem of sequential change-point detection in multiparameter exponential families when the pre- and post-change parameters are unknown. In this connection we also address certain issues recently raised by Mei (2008) concerning performance criteria for detection rules in this setting.

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Sequential Change-Point Detection When the Pre- and Post-Change Parameters are Unknown

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