Results 221 to 230 of about 56,489 (266)
Some of the next articles are maybe not open access.
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
openaire +2 more sources
On inference for threshold autoregressive models
Test, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stramer, Osnat, Lin, Yu-Jau
openaire +1 more source
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
openaire +2 more sources
Grouped spatial autoregressive model
Computational Statistics & Data Analysis, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Danyang Huang +3 more
openaire +3 more sources
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 ...
openaire +3 more sources
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
openaire +2 more sources
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
openaire +1 more source
Analysis of Multistate Autoregressive Models
IEEE Transactions on Signal Processing, 2018In this paper, we consider the inference problem for a wide class of time-series models, referred to as multistate autoregressive models. The time series that we consider are composed of multiple epochs, each modeled by an autoregressive process. The number of epochs is unknown, and the transitions of states follow a Markov process of an unknown order.
Jie Ding 0002 +3 more
openaire +2 more sources
Bayesian mixture of autoregressive models
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
John W. Lau, Mike K. P. So
openaire +4 more sources
On the Autoregressive Model with Random Coefficients
Calcutta Statistical Association Bulletin, 1983The first and second order stationarity conditions for an autore-gressive model with random coefficients are obtained. In addition, for such a type of model, the asymptotic mean squared error of an h-step ahead forecast is also considered.
openaire +1 more source