Sequential change-point detection for mixing random sequences under composite hypotheses

被引：0
作者：
Brodsky B. [1 ]
Darkhovsky B. [2 ]
机构：
[1] Central Institute for Mathematics and Economics, RAS, 117418 Moscow, 47, Nakhimovsky prospekt
[2] Institute for Systems Analysis, RAS, Oktyabria, 117312 Moscow, 9
来源：
Statistical Inference for Stochastic Processes | 2008年 / 11卷 / 1期
关键词：
Change-point problem; Composite hypotheses;
D O I：
10.1007/s11203-006-9004-6
中图分类号：
学科分类号：
摘要：
The problem of sequential detection of a change-point in the density function of one-dimensional distribution of observations from a mixing random sequence is considered when both before and after a change-point this density function belongs to a certain family of distributions, i.e. in the situation of composite hypotheses. A new quality criterion for change-point detection is proposed. The asymptotic a priori lower bound for this criterion is proved for wide class of methods of change-point detection. An asymptotically optimal method of change-point detection is proposed for which this lower bound is attained asymptotically. In particular, for the case of a simple hypothesis before a change-point, this method coincides with the generalized cumulative sums (CUSUM) method. © 2006 Springer Science+Business Media, LLC.
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页码：35 / 54
页数：19
相关论文
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