Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1148215 | Journal of Statistical Planning and Inference | 2008 | 14 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Manuel Salvador, Pilar Gargallo,