Bayesian monitoring of local residual autocorrelations taking into account the run-length |
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Authors: | Manuel Salvador Pilar Gargallo |
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Institution: | Department of Statistical Methods, Faculty of Economics and Business Studies, University of Zaragoza, Gran Via 2, Zaragoza 50005, Spain |
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Abstract: | This paper proposes new two-sided monitoring algorithms for detecting the presence of first order residual autocorrelations in Dynamic Normal Models. The methodology uses a Bayesian decision approach with loss function which takes into account the run-length of the process. The power and mean run-length of the proposed algorithms are analysed by Monte Carlo methods. The results obtained improve those corresponding to the monitoring algorithm for residual autocorrelations proposed in Gargallo and Salvador 2003. Monitoring residual autocorrelations in dynamic linear models. Comm. Statist. Simulation Comput. 32(4), 1079–1104.] with respect to the run-length, and also exhibit more homogeneous behaviour. |
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Keywords: | Sequential monitoring Dynamic Linear Models Bayesian inference |
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