On higher spectral densities of stationary processes with mixing

1976 ◽  
Vol 27 (4) ◽  
pp. 364-373 ◽  
Author(s):  
I. G. Zhurbenko ◽  
N. M. Zuev
Keyword(s):  
Stationary Processes ◽  
Spectral Densities

Estimation of spectral densities of stationary processes

1979 ◽  
Vol 19 (1) ◽  
pp. 47-60
Author(s):  
I. G. Zhurbenko ◽  
N. N. Trush
Keyword(s):  
Stationary Processes ◽  
Spectral Densities

CLUSTER ANALYSIS FOR NON-GAUSSIAN LOCALLY STATIONARY PROCESSES

2006 ◽  
Vol 09 (01) ◽  
pp. 113-132 ◽  
Author(s):  
JUNICHI HIRUKAWA
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Integral Functional ◽  
Stationary Time Series ◽  
Series Data ◽  
Stationary Processes ◽  
Suitable Model ◽  
Time Varying ◽  
Spectral Densities ◽  
Locally Stationary Processes

Time series analysis under stationary assumption has been well established. However, stationary time series models are not plausible to describe the real world. Indeed, relatively long stretches of time series data should contain either slow or rapid changes in the spectra. To develop a general non-stationary theory, we have to pay careful attention to constituting a suitable model, otherwise the observations obtained in the future give no information about the present structure. Dahlhaus [1–4] has introduced an important class of non-stationary processes, called locally stationary processes which have the time varying spectral densities. In this paper, for a clustering problem of stock returns in Tokyo Stock Exchanges, we propose nonparametric approach based on generalized integral functional measures of the time varying spectral densities. The generalized measures include Gaussian Kullback–Leibler information and Chernoff information measures. The clustering results well extract the features of the relationship among the companies.

On estimation of parameters of Gaussian stationary processes

1979 ◽  
Vol 16 (03) ◽  
pp. 575-591 ◽  
Author(s):  
Masanobu Taniguchi
Keyword(s):  
Asymptotic Behavior ◽  
Spectral Density ◽  
Stationary Process ◽  
Parametric Family ◽  
Stationary Processes ◽  
Estimation Of Parameters ◽  
Spectral Densities ◽  
Gaussian Stationary Process

In fitting a certain parametric family of spectral densities fθ (x) to a Gaussian stationary process with the true spectral density g (x), we propose two estimators of θ, say by minimizing two criteria D 1 (·), D 2(·) respectively, which measure the nearness of fθ (x) to g (x). Then we investigate some asymptotic behavior of with respect to efficiency and robustness.

On estimation of parameters of Gaussian stationary processes

1979 ◽  
Vol 16 (3) ◽  
pp. 575-591 ◽  
Author(s):  
Masanobu Taniguchi
Keyword(s):  
Asymptotic Behavior ◽  
Spectral Density ◽  
Stationary Process ◽  
Parametric Family ◽  
Stationary Processes ◽  
Estimation Of Parameters ◽  
Spectral Densities ◽  
Gaussian Stationary Process

In fitting a certain parametric family of spectral densities fθ (x) to a Gaussian stationary process with the true spectral density g (x), we propose two estimators of θ, say by minimizing two criteria D1 (·), D2(·) respectively, which measure the nearness of fθ (x) to g (x). Then we investigate some asymptotic behavior of with respect to efficiency and robustness.

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