Results 121 to 130 of about 45,001 (165)
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Autoregressive modeling of the Wigner spectrum
ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005Autoregressive(AR) or linear predictive(LP) modeling and Wigner time-frequency representations have been proposed for non-stationary signal analysis and synthesis, owing to their specific advantages over the short-time Fourier transform, viz. reduced data set characterisation and improved frequency resolution of the former, and the improved time ...
P. A. Ramamoorthy +2 more
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Tutorial on Multivariate Autoregressive Modelling
Journal of Clinical Monitoring and Computing, 2006In the present paper, the theoretical background of multivariate autoregressive modelling (MAR) is explained. The motivation for MAR modelling is the need to study the linear relationships between signals. In biomedical engineering, MAR modelling is used especially in the analysis of cardiovascular dynamics and electroencephalographic signals, because ...
Heli, Hytti +2 more
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On inference for threshold autoregressive models
Test, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stramer, Osnat, Lin, Yu-Jau
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THE IDENTIFICATION OF SEASONAL AUTOREGRESSIVE MODELS
Journal of Time Series Analysis, 1995Abstract.In this paper we present a new approach for identifying seasonal autoregressive models and the degree of differencing required to induce stationarity in the data. The identification method is iterative and consists in systematically fitting increasing order models to the data and then verifying that the resulting residuals behave like white ...
Koreisha, Sergio G., Pukkila, Tarmo
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Modelling of cointegration in the vector autoregressive model
Economic Modelling, 2000Abstract A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends are defined. The statistical model for cointegrated I (1) variables is defined, and it is shown how hypotheses on the cointegrating relations can be estimated under suitable ...
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Exact Geometry of Autoregressive Models [PDF]
Exact expressions for the statistical curvature and related geometric quantities in firstāorder autoregressive models are derived. We present a method for calculating moments that is applicable in general autoregressive models. It combines the algebra of differential and difference operators to simplify the problem, and to obtain results valid for all ...
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Functional Threshold Autoregressive Model
Statistica SinicaSummary: We propose a functional threshold autoregressive model for flexible functional time series modeling. In particular, the behavior of a function at a given time point can be described by different autoregressive mechanisms, depending on the values of a threshold variable at a past time point. Sufficient conditions for the strict stationarity and
Li, Yuanbo +3 more
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Autoregressive video conference models
International Journal of Network Management, 2004AbstractVideo conferencing is an important application that has been extensively used in IP, ATM networks, and TV broadcasting as a means of interactive communications. Teleconferencing video traffic consists of video scenes in which one or more people are talking with low to medium motion and almost unchanged background.
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Order selection of autoregressive models
IEEE Transactions on Signal Processing, 1992The problem of determining the order of autoregressive models by Bayesian predictive densities is addressed. A criterion employing noninformative prior densities of the model parameters is derived. Simulation results which demonstrate the good performance of the criterion are presented. Comparisons with four popular approaches verify its superiority in
Petar M. Djuric, Steven M. Kay
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A test of nonlinear autoregressive models
ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003A study on testing the appropriateness of a particular structure selection and design for block-oriented nonlinear models is presented. Block-oriented nonlinear models characterize some features of Volterra kernels and extract only particular higher-order statistical information.
Shiyi Mao, Pinxing Lin
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