Asymptotically optimal methods for sequential change-point detection

asymptotic optimality;change-point detection;decentralized decision;multi-sensor;sequential detection;sequential testing

Mei, Yajun ; Lorden, Gary A.

University:California Institute of Technology

Department:Physics, Mathematics and Astronomy

关键词: asymptotic optimality;    change-point detection;    decentralized decision;    multi-sensor;    sequential detection;    sequential testing;   

Others  :  https://thesis.library.caltech.edu/2231/1/mei_thesis.pdf

美国|英语

来源: Caltech THESIS

【 摘 要 】

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.