The order determination problem for linear time-varying AR models

Kozin, F.; Nakajima, Fumio

doi:10.1109/tac.1980.1102318

Cited by 57 publications

(13 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are few techniques in choice of TVAR model order. For instance, Bayesian technique [15] and Akaike information criterion (AIC) [16] are used for the determination of model orders in TVAR models. In this article, we consider the choice of model order as a Maximumlikelihood (ML) estimation [17] technique.…”

Section: Order Selectionmentioning

confidence: 99%

Performance Analysis of Basis Functions in TVAR Model

Reddy¹,

Rao²

2014

IJSIP

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Section: Order Selectionmentioning

confidence: 99%

Performance Analysis of Basis Functions in TVAR Model

Reddy¹,

Rao²

2014

IJSIP

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“…For models assumed under different order, the order corresponded to the minimum of the AIC is the appropriate order. For the nostationary random signal, KonzinF [9] analyzed the sufficient condition and researched the application of the AIC criterion in identifying the order of the nostationary random signal model. He found that these problems and parameter estimations were uniform in progressive normal state.…”

Section: Order Identificationmentioning

confidence: 99%

Time-Frequency Analysis Based on PNN for Nonstationary Random Vibration of Spacecraft

Yang¹,

Cheng²,

Hong³

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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In view of the disadvantages of the traditional time-varying algorithm about nonstationary random vibration signal of a spacecraft with close spaced modal frequency. A process neural network (PNN) based on the empirical mode decomposition (EMD) method is put forward. First, the EMD method is utilized to decompose the signal into several intrinsic mode functions (IMFs). Then for each IMF, The PNN is established and time-varying auto-spectral density is obtained. Finally, the time-varying auto-spectral density of the signal can be reconstituted by linear superposing. The example results show the effectiveness of this new method in time-frequency analysis.

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“…Proposal of generalized criteria, based on Kozin and Nakajima (1980) Determination of the order of autoregressive models which "best" approximate stationary time series is discussed and a criterion (CAT) to be used for this purpo~Je is proposed Parzen (1977) x x Generalization of the CAT criterion to multivariate autoregresslons Picci (1982) x x x A tutorial paper on model parametrization and model structure selection in mul ti- Diaqnostic tests of Lagrange anrl "portTremayne ( 1982) manteau" type for multivariable ARMA models are discussed Rissanen (1976) x x…”

Section: Pztmentioning

confidence: 99%