Volume 16, Issue 1 (6-2017)                   JIRSS 2017, 16(1): 77-94 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Gholami G. On the Bayesian Sequential Change-Point Detection. JIRSS 2017; 16 (1) :77-94
URL: http://jirss.irstat.ir/article-1-381-en.html
Department of Mathematics, Faculty of Sciences, Urmia University, Iran , gh.gholami@urmia.ac.ir
Abstract:   (6845 Views)

The problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance and signal detection. We discuss a Bayesian approach in the context of statistical process control: at an unknown time  τ, the process behavior changes and the distribution of the data changes from p0 to p1. Two cases are considered: (i) p0 and p1 are fully known, (ii) p0 and p1 belong to the same family of distributions with some unknown parameters θ1≠θ2. We present a maximum a posteriori estimate of the change-point which, for the case (i), can be computed in a sequential manner. In addition, we propose the use of the Shiryaev's loss function. Under this assumption, we define a Bayesian stopping rule. For the Poisson distribution and in the two cases (i) and (ii), we obtain results for the conjugate prior.

Full-Text [PDF 135 kb]   (2356 Downloads)    
Type of Study: Original Paper | Subject: 62Jxx: Linear inference, regression
Received: 2016/09/15 | Accepted: 2016/09/15 | Published: 2016/09/15

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Journal of The Iranian Statistical Society