AUTOMATIC INFERENCE FOR INFINITE ORDER VECTOR AUTOREGRESSIONS

2005 ◽  
Vol 21 (01) ◽  
Author(s):  
Guido M. Kuersteiner
Keyword(s):  
Infinite Order ◽  
Vector Autoregressions ◽  
Automatic Inference

scholarly journals Automatic inference of movements from contact histories

2011 ◽  
Vol 41 (4) ◽  
pp. 386-387 ◽  
Author(s):  
Pengcheng Wang ◽  
Zhaoyu Gao ◽  
Xinhui Xu ◽  
Yujiao Zhou ◽  
Haojin Zhu ◽  
...  
Keyword(s):  
Automatic Inference

scholarly journals Identification of an unstable ARMA equation

2001 ◽  
Vol 7 (1) ◽  
pp. 97-112 ◽  
Author(s):  
Yulia R. Gel ◽  
Vladimir N. Fomin
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Infinite Order ◽  
Time Series Model ◽  
Identification Algorithm ◽  
Data Handling ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Strongly Consistent ◽  
Minimal Phase

Usually the coefficients in a stochastic time series model are partially or entirely unknown when the realization of the time series is observed. Sometimes the unknown coefficients can be estimated from the realization with the required accuracy. That will eventually allow optimizing the data handling of the stochastic time series.Here it is shown that the recurrent least-squares (LS) procedure provides strongly consistent estimates for a linear autoregressive (AR) equation of infinite order obtained from a minimal phase regressive (ARMA) equation. The LS identification algorithm is accomplished by the Padé approximation used for the estimation of the unknown ARMA parameters.

On entire functions of infinite order

1963 ◽  
Vol 14 (1) ◽  
pp. 323-327 ◽  
Author(s):  
S. M. Shah
Keyword(s):  
Infinite Order ◽  
Entire Functions
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