Showing papers on "STAR model published in 1973"
TL;DR: In this article, the authors proposed a nonparametric spectrum factorization method for time series, where the model is formulated explicitly in terms of the spectral density function, and may also be viewed as a spectrum factorisation method.
Abstract: SUMMARY Existing procedures for obtaining linear prediction formulae for time series may be placed in two categories. The first consists of methods which involve fitting parametric models of the autoregressive or autoregressive moving average type. The second involves the factorization of an estimated spectral density function; this is essentially a nonparametric procedure. The procedure suggested in this paper contains elements of both of the above. Formally, it involves fitting a model with a finite number of parameters. However, the model is formulated explicitly in terms of the spectral density function, and may also be viewed as a spectrum factorization method. The model has been fitted to various series considered by other authors, and the quality of the fit is compared with that obtained with conventional models using the same number of parameters. Asymptotie properties of the parameter estimates have been used to assess the nonparametric spectrum factorization techniques proposed by other authors.
580 citations
TL;DR: This paper studies an alternative parametrization of autoregressive models of finite order, namely the parametsrization by the partial autocorrelations, which is shown to vary freely from -1 to +1 and to be in a one-to-one, continuously differentiable correspondence with the regression parameters.
172 citations
TL;DR: In this paper, an empirical study of the application of Akaike's final predictor error (FPE) criterion to the estimation of the order of finite autoregressive models to infinite auto-gressive model scheme data and the subsequent application of those models to spectral estimation are given.
Abstract: Results of an empirical study of the application of Akaike's final predictor error (FPE) criterion to the estimation of the order of finite autoregressive models to infinite autoregressive model scheme data and the subsequent application of those models to spectral estimation are given.
86 citations
26 citations
TL;DR: In this article, a rather extensive empirical study of autoregressive approximations to first-order moving averages, and in particular their use in estimating the moving average coefficient, shows a significant bias to be present.
Abstract: SUMMARY A rather extensive empirical study of autoregressive approximations to first-order moving averages, and in particular their use in estimating the moving average coefficient, shows a significant bias to be present. This is of special importance now that autoregressive spectral estimators are becoming popular. Several conjectures are made about the cause of bias, and some procedures are developed to reduce this bias.
12 citations
TL;DR: Some issues raised by claims for the use of an EEG autoregressive model for the time-saving calculation of spectral power density are discussed and a critical review is undertaken.
9 citations
TL;DR: In this paper, alternative systems of negative binomial and binomial weights are proposed for measuring the permanent component of a series as a weighted average of the observed series, if the series is generated by a stable first-order autoregressive process.
Abstract: Alternative systems of negative binomial and binomial weights are proposed for measuring the permanent component of a series as a weighted average of the observed series. If the observed series is generated by a stable first-order autoregressive process it is shown that there are alternative sets of weights using either weighting scheme for which the permanent and transitory components are uncorrelated.
3 citations