Results 231 to 240 of about 10,065 (268)

State representations of ARMA-models

International Journal of Control, 2010
A state representation of an arbitrary ARMA-model is computed explicitly. It is shown then that every ARMA-model is homotopy equivalent to its state representation, and that two state models are homotopy equivalent if and only if they are similar.
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Modelling and Forecasting with ARMA Processes

1996
The determination of an appropriate ARMA(p, q) model to represent an observed stationary time series involves a number of interrelated problems. These include the choice of p and q (order selection) and estimation of the mean, the coefficients {ϕ i , i = 1, …, p}, {θ i , i = 1, …, q}, and the white noise variance σ2.
Peter J. Brockwell, Richard A. Davis
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A NOTE ON EMBEDDING A DISCRETE PARAMETER ARMA MODEL IN A CONTINUOUS PARAMETER ARMA MODEL

Journal of Time Series Analysis, 1987
Abstract. We have shown that it is not always possible to embed a real‐valued discrete parameter Gaussian AR(1) model in a real‐valued continuous parameter Gaussian AR(1). The problem with general ARMA models is also discussed.
Chan, K. S., Tong, H.
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ARMA MODELS WITH ARCH ERRORS

Journal of Time Series Analysis, 1984
Abstract.This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and incorporated into techniques for model building based upon the application of the usual Box‐Jenkins methodology of identification, estimation and diagnostic ...
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ARMA MODELLING WITH NON‐GAUSSIAN INNOVATIONS

Journal of Time Series Analysis, 1988
Abstract.The problem of modelling time series driven by non‐Gaussian innovations is considered. The asymptotic normality of the maximum likelihood estimator is established under some general conditions. The distribution of the residual autocorrelations is also obtained. This gives rise to a potentially useful goodness‐of‐fit statistic.
McLeod, AI, Li, WK
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The quality of models for ARMA processes

IEEE Transactions on Signal Processing, 1998
The model error (ME) is an objective measure for assessing the quality of different models of a given ARMA process. The expression for ME can be evaluated easily in the time domain. This quality measure for known and given processes is necessary for an objective comparison of the performance of estimation algorithms and of order selection criteria.
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ARMA Models

2022
Wayne A. Woodward, Bivin P. Sadler, Stephen D. Robertson   +2 more
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ARMA models

2020
Sotiris Tsolacos, Mark Andrew
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Identification of ARMA models with binary-valued observations

Automatica, 2023
Jin Guo, Yanlong Zhao
exaly  

Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models

Journal of the American Statistical Association, 1984
Ruey S Tsay, George C Tiao
exaly   +2 more sources