Results 211 to 220 of about 12,105 (264)
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The Identification of ARMA Models
Biometrika, 1990SUMMARY We present a new powerful method for determining the order of ARMA (p, q) models having small sets of observations. The procedure is based on an autoregressive order determination criterion and on linear estimation methods. Simulated data are used to demonstrate the capabilities of the approach. for a survey.
TARMO PUKKILA +2 more
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ARMA Model and Wavelet-Based ARMA Model Application
Applied Mechanics and Materials, 2011This article discusses some of the linear regression model from the modeling theory, focusing on the modeling method of selecting the optimal model, choose the best bandwidth criteria. Then, given some of the partial linear regression model from the estimates and the partial residual nuclear smooth estimates, and estimate the model using the partial ...
Wei Wei, Hao Ma
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1986
This paper deals with the ubiquitous class of linear time-invariant finite dimensional systems. In [1] it has been shown that this is precisely the class of AR systems, that is, the dynamical systems whose behavior is governed by a finite set of autoregressive relations.
NIEUWENHUIS, JW, WILLEMS, JC
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This paper deals with the ubiquitous class of linear time-invariant finite dimensional systems. In [1] it has been shown that this is precisely the class of AR systems, that is, the dynamical systems whose behavior is governed by a finite set of autoregressive relations.
NIEUWENHUIS, JW, WILLEMS, JC
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The Misspecification of Arma Models
Statistica Neerlandica, 1989The object of this paper is to assess the effects of fitting a model of the wrong order to a time series which is generated by an autoregressive moving–average process. The method is to examine the spectral density functions which are indicated by the probability limits of the least–squares estimators of the misspecified models.
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ARMA-models and their equivalences
International Journal of Control, 2009The classical theory of ‘strict system equivalence’ of Rosenbrock and Fuhrmann is presented in a very general setting, namely, in the setting of ARMA-models defined over an arbitrary commutative ring.
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
A method for efficiently generating a rational model of a wide-sense stationary time series is presented. In this method the autoregressive parameters associated with an ARMA model consisting of q zeros and p poles are optimally chosen with the selection being based on a finite set of time series observations.
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A method for efficiently generating a rational model of a wide-sense stationary time series is presented. In this method the autoregressive parameters associated with an ARMA model consisting of q zeros and p poles are optimally chosen with the selection being based on a finite set of time series observations.
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ON EMBEDDING A DISCRETE‐PARAMETER ARMA MODEL IN A CONTINUOUS‐PARAMETER ARMA MODEL
Journal of Time Series Analysis, 1989Abstract. It is shown that a real‐valued discrete‐parameter Gaussian ARMA (p. q) model with q < p can be embedded in a real‐valued continuous‐parameter Gaussian ARMA(p', q') model with q' < p'. The problem of embedding a real‐valued discrete‐parameter Gaussian AR(p) into a real‐valued continuous‐parameter Gaussian AR(p) is also discussed.
He, S. W., Wang, J. G.
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Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
We present some aspects of non-Gaussian H-ARMA models. After recalling that an H-ARMA process is obtained by passing an ARMA process through a Hermite polynomial nonlinearity, we describe the theoretical analysis of their cumulants and cumulant spectra. The main advantage of this kind of model is that the cumulant structure of the output can be deduced
David Declercq, Patrick Duvaut
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We present some aspects of non-Gaussian H-ARMA models. After recalling that an H-ARMA process is obtained by passing an ARMA process through a Hermite polynomial nonlinearity, we describe the theoretical analysis of their cumulants and cumulant spectra. The main advantage of this kind of model is that the cumulant structure of the output can be deduced
David Declercq, Patrick Duvaut
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DIFFERENTIAL GEOMETRY OF ARMA MODELS
Journal of Time Series Analysis, 1990Abstract.A general approach for the development of a statistical inference on autoregressive moving‐average (ARMA) models is presented based on geometric arguments. ARMA models are characterized as members of the curved exponential family. Geometric properties of ARMA models are computed and used to suggest parameter transformations that satisfy ...
Ravishanker, Nalini +2 more
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A recursive procedure for ARMA modeling
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985This paper presents a two-part fast recursive algorithm for ARMA modeling. The algorithm first obtains estimates of the p autoregressive coefficients from a set of p extended Yule-Walker equations. An exact recursive lattice algorithm for this estimator is then derived.
Randolph L. Moses +2 more
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