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

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.

Keywords:

• Asymptotic optimality
• Bayes
• Detection delay
• False alarm rate
• Generalized likelihood ratio

Subject Classifications:

• 62L12
• 62F15
• 62M05

ACKNOWLEDGMENTS

Lai's research is supported by the National Science Foundation under grant DMS-0805879. Xing's research is supported by the National Science Foundation under grant DMS-0906593.

Notes

Recommended by A. G. Tartakovsky