Statistical predictor identification

@article{Akaike1970StatisticalPI,
  title={Statistical predictor identification},
  author={Hirotugu Akaike},
  journal={Annals of the Institute of Statistical Mathematics},
  year={1970},
  volume={22},
  pages={203-217}
}
  • H. Akaike
  • Published 1 December 1970
  • Mathematics
  • Annals of the Institute of Statistical Mathematics
In a recent paper by the present author [1] a simple practical procedure of predictor identification has been proposed. It is the purpose of this paper to provide a theoretical and empirical basis of the procedure. 
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References

SHOWING 1-8 OF 8 REFERENCES
Prediction of Multivariate Time Series
Abstract A method of linear prediction of stationary multivariate time series is discussed from the point of view of meteorological applications. Tests of significance are given and it is shown by
Fitting autoregressive models for prediction
This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models. The use of autoregressive representation
Some probability limit theorems with statistical applications
In fundamental papers Bernstein (3) and Loeve(8) have proved central limit theorems for wide classes of dependent variables. Their theorems are stated in terms of conditional distributions. In the