A Bayesian Approach to Sequential Surveillance in Exponential Families

Tze Leung Lai, Tongwei Liu and Haipeng Xing

Communications in Statistics - Theory and Methods, 2009, vol. 38, issue 16-17, 2958-2968

Abstract: We describe herein a Bayesian change-point model and the associated recursive formulas for the estimated time-varying parameters and the posterior probability that a change-point has occurred at a particular time. The proposed model is a variant of that of Chernoff and Zacks (1964) for the case of normal means with known common variance. It considers more generally the multiparameter exponential family and addresses the complex statistical issues due to multiple change-points and unknown pre- and post-change system parameters in sequential surveillance. A sequential detection rule based on the proposed model is also introduced and its false alarm rate and mean detection delay are studied in the multiple change-point setting.

Date: 2009
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DOI: 10.1080/03610920902947253

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