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- W2115737071 abstract "We study the problem of parameter identifiability with Kullback–Leibler information divergence (KLID) criterion. The KLID-identifiability is defined, which can be related to many other concepts of identifiability, such as the identifiability with Fisher's information matrix criterion, identifiability with least-squares criterion, and identifiability with spectral density criterion. We also establish a simple check criterion for the Gaussian process and derive an upper bound for the minimal identifiable horizon of Markov process. Furthermore, we define the asymptotic KLID-identifiability and prove that, under certain constraints, the KLID-identifiability will be a sufficient or necessary condition for the asymptotic KLID-identifiability. The consistency problems of several parameter estimation methods are also discussed. Copyright © 2008 John Wiley & Sons, Ltd." @default.
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- W2115737071 date "2009-10-01" @default.
- W2115737071 modified "2023-09-24" @default.
- W2115737071 title "Parameter identifiability with Kullback-Leibler information divergence criterion" @default.
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- W2115737071 doi "https://doi.org/10.1002/acs.1078" @default.
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