Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17207
Title: System uncertainty and statistical detection for jump-diffusion models
Authors: Huang, J 
Li, X 
Keywords: Bayes factor
Jump-diffusion process
Markov chain approximation
System uncertainty
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on automatic control, 2010, v. 55, no. 3, 5393008, p. 697-702 How to cite?
Journal: IEEE transactions on automatic control 
Abstract: Motivated by the common-seen model uncertainty of real-world systems, we propose a likelihood ratio-based approach to statistical detection for a rich class of partially observed systems. Here, the system state is modeled by some jump-diffusion process while the observation is of additive white noise. Our approach can be implemented recursively based on some Markov chain approximation method to compare the competing stochastic models by fitting the observed historical data. Our method is superior to the traditional hypothesis test in both theoretical and computational aspects. In particular, a wide range of different models can be nested and compared in a unified framework with the help of Bayes factor. An illustrating numerical example is also given to show the application of our method.
URI: http://hdl.handle.net/10397/17207
ISSN: 0018-9286
EISSN: 1558-2523
DOI: 10.1109/TAC.2009.2037456
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