DOI: 10.18869/acadpub.jirss/20170601

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Department of Mathematics, Faculty of Sciences, Urmia University, Iran
Abstract:   (851 Views)
The problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance. We discuss a Bayesian approach in the context of statistical process control: at an unknown time  $tau$, 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.
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Type of Study: Original Paper | Subject: 62Jxx: Linear inference, regression
Received: 2016/09/15 | Accepted: 2016/09/15 | Published: 2016/09/15