State estimation of a supply chain using improved resampling rules for particle filtering

Proceedings - Winter Simulation Conference

Volumn , Issue , 2010, Pages 1998-2010

  Celik, Nurcin ^a   Son, Young Jun ^a  

^a University of Arizona   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL RESOURCES; HALF-WIDTH; IMPORTANCE SAMPLING; PARTICLE FILTER; PARTICLE FILTERING; RESAMPLING; ROOT MEAN SQUARE ERRORS; SAMPLING PROCEDURES; SINGLE PARTICLE; SUPPLY CHAIN SIMULATION;

NONLINEAR FILTERING; STATE ESTIMATION; SUPPLY CHAINS;

SUPPLY CHAIN MANAGEMENT;

EID: 79951596157     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2010.5678871     Document Type: Conference Paper

References (25)

1. Anscombe, F.J. 1952. Large sample theory of sequential estimation. Proceedings of the Cambridge Philosophical Society, 48, 600-607.
2. Arnaud, E. and Le Gland, F. 2009. SMC with adaptive resampling : large sample asymptotics, Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing, Cardiff, August 31-September 3, 2009, 481-48.
3. Bayraksan, G. 2005. Monte Carlo Sampling-Based Methods in Stochastic Programming, Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin.
4. Brasnett. P. L. Mihaylova, D. Bull, and N. Canagarajah. 2007. Sequential Monte Carlo Tracking by Fusing Multiple Cues in Video Sequences. Image and Vision Computing, Elsevier Science, 28 (1), 1217-1227. (Pubitemid 46764422)
5. Casarin, R. and D. Sartore. 2008. Matrix-State Particle Filter for Wishart Stochastic Volatility Processes, Discussion Paper (No: 0816), Dipartimento di Scienze Economiche, Università degli Studi di Brescia, Italy.
6. Celik, N., and Y-J. Son. 2010. Sequential Monte Carlo-based Fidelity Selection in Dynamic-Data-Driven Adaptive Multi-Scale Simulations, submitted to International Journal of Production Research.
7. Douc, R., and O. Cappe. 2005. Comparison of resampling schemes for particle filtering, Proceedings of the 4th International Symposium on In Image and Signal Processing and Analysis, 64-69. (Pubitemid 44459664)
8. Doucet, A., N. De Freitas, and N. Gordon. 2001. Eds. Sequential Monte Carlo Methods in Practice, Springer, New York, 2001.
9. Flury, T. and N. Shephard. 2008. Bayesian inference based only on simulated likelihood: Particle filter analysis of dynamic economic models, Report (No: 413), Department of Economics, University of Oxford, UK.
10. Goodman, L. A., 1960, On the exact variance of products, Journal of the American Statistical Association, 55 (292), 708-713.
11. Guterman, M. M., and Z.H. Nitecki. 2006. Differential Equations-A First Course, Delhi:Indo American Books.
12. Liu J.S., and R. Chen. 1998. Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, 93 (443), 1032-1044.
13. Kitagawa, G. 1996. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, Journal of Computational and Graphical Statistics, 5, 1-25.
14. Kong, A. 1992. A note on importance sampling using standardized weights, Technical Report, 348.
15. Kong, A., J.S. Liu, and W.H. Wong. 1994. Sequential imputations and Bayesian missing data problems, Journal of American Statistical Association, 89, 278-288.
16. Koop, J. C., 1951. A note on the bias of the ratio estimate, Bulletin of the International Statistical Institute., 21(2), 141-146.
17. Koop, J. 1962. On upper limits to the difference in bias between two ratio estimates, Metrika, (5) 1, 145- 149.
18. Meng, X.L. 1993. On the absolute bias ratio of ratio estimators, Statistics and Probability Letters, 18, 345-348.
19. Patterson, D. 2008. The Delta method and applications, Report.
20. Raj, D. 1964. A note on the variance of the ratio estimate, Journal of the American Statistical Association, 59 (307), 895-898.
21. Randall, D. A. 2006. Taylor Series, Technical Report.
22. Ristic, B. M. Arulampalam, and A. Gordon. 2004. Beyond Kalman Filters: Particle Filters for Target Tracking. Artech House.
23. Schulz, D., and W. Burgard. 2001. Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems, 34 (2-3).
24. Tin, M. 1965, Comparison of some ratio estimators, Journal of the American Statistical Association, 60(309), 294-307 .
25. Xu, X. and B. Li. 2007. Adaptive Rao-Blackwellised Particle Filter and its Evaluation for Tracking in Surveillance. IEEE Transactions on Image Processing, 16 (3), 838-849.