论文信息 - Using Periodic Autoregressions for Multiple Spectral Estimation

Using Periodic Autoregressions for Multiple Spectral Estimation

A new method of estimating the spectral density of a multiple time series based on the concept of periodically stationary autoregressive processes is described and illustrated. It is shown that the method can often overcome some difficulties inherent in the traditional smoothed periodogram and autoregressive spectral-estimation methods and that additional insights into the structure of a multiple time series can be obtained by using periodic autoregressions.

H. Newton | H. Joseph Newton

[1] Calyampudi Radhakrishna Rao,et al. Linear Statistical Inference and its Applications , 1967 .

[2] D. G. Watts,et al. Spectral analysis and its applications , 1968 .

[3] E. Parzen. MULTIPLE TIME SERIES MODELLING. , 1968 .

[4] Marcello Pagano,et al. On Periodic and Multiple Autoregressions , 1978 .

[5] R H Jones,et al. Time series with periodic structure. , 1967, Biometrika.

[6] David R. Brillinger,et al. Time Series: Data Analysis and Theory. , 1982 .

[7] H. Akaike. A new look at the statistical model identification , 1974 .

[8] Calyampudi R. Rao,et al. Linear Statistical Inference and Its Applications. , 1975 .

[9] E. J. Hannan,et al. Multiple time series , 1970 .

[10] E. Parzen. On Consistent Estimates of the Spectrum of a Stationary Time Series , 1957 .

[11] E. Parzen. Multiple Time Series: Determining the Order of Approximating Autoregressive Schemes. , 1975 .

[12] K. Berk. Consistent Autoregressive Spectral Estimates , 1974 .

[13] M. Pagano,et al. On the stationarity of multiple autoregressive approximants: theory and algorithms , 1981 .