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Asymptotically optimal methods for sequential change-point detection


Mei, Yajun (2003) Asymptotically optimal methods for sequential change-point detection. Dissertation (Ph.D.), California Institute of Technology.


This thesis studies sequential change-point detection problems in different contexts. Our main results are as follows:

- We present a new formulation of the problem of detecting a change of the parameter value in a one-parameter exponential family. Asymptotically optimal procedures are obtained.

- We propose a new and useful definition of ?asymptotically optimal to first-order? procedures in change-point problems when both the pre-change distribution and the post-change distribution involve unknown parameters. In a general setting, we define such procedures and prove that they are asymptotically optimal.

- We develop asymptotic theory for sequential hypothesis testing and change-point problems in decentralized decision systems and prove the asymptotic optimality of our proposed procedures under certain conditions.

- We show that a published proof that the so-called modified Shiryayev-Roberts procedure is exactly optimal is incorrect. We also clarify the issues involved by both mathematical arguments and a simulation study. The correctness of the theorem remains in doubt.

- We construct a simple counterexample to a conjecture of Pollak that states that certain procedures based on likelihood ratios are asymptotically optimal in change-point problems even for dependent observations.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:asymptotic optimality; change-point detection; decentralized decision; multi-sensor; sequential detection; sequential testing
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Mathematics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Lorden, Gary A.
Thesis Committee:
  • Lorden, Gary A. (chair)
  • Wales, David B.
  • Candes, Emmanuel J.
  • Sherman, Robert P.
Defense Date:28 May 2003
Non-Caltech Author Email:myajun (AT)
Record Number:CaltechETD:etd-05292003-133431
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2231
Deposited By: Imported from ETD-db
Deposited On:30 May 2003
Last Modified:26 Dec 2012 02:48

Thesis Files

PDF (mei_thesis.pdf) - Final Version
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