State estimation of a shop floor using improved resampling rules for particle filtering
Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- J. Koop, 1962. "On upper limits to the difference in bias between two ratio estimates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 5(1), pages 145-149, January.
- Neil Shephard & Thomas Flury, 2008.
"Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models,"
Economics Series Working Papers
413, University of Oxford, Department of Economics.
- Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(05), pages 933-956, October.
- Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series 2008fe32, Oxford Financial Research Centre.
- Lee, Chih-Ming, 2008. "A Bayesian approach to determine the value of information in the newsboy problem," International Journal of Production Economics, Elsevier, vol. 112(1), pages 391-402, March.
- Surekha, K. & Ghali, Moheb, 2001. "The speed of adjustment and production smoothing: Bayesian estimation," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 55-65, May.
- Paul Fearnhead & Peter Clifford, 2003. "On-line inference for hidden Markov models via particle filters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 887-899.
- Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
- Meng, Xiao-Li, 1993. "On the absolute bias ratio of ratio estimators," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 345-348, December.
- Roberto Casarin & Domenico sartore, 2008.
"Matrix-State Particle Filter for Wishart Stochastic Volatility Processes,"
0816, University of Brescia, Department of Economics.
- Roberto Casarin & Domenico Sartore, 2007. "Matrix-State Particle Filter for Wishart Stochastic Volatility Processes," Working Papers 2007_30, Department of Economics, University of Venice "Ca' Foscari".
When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:224-237. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.