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 1970
  • Annals of the Institute of Statistical Mathematics
Asymptotically Efficient Model Selection for Panel Data Forecasting
This paper develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic
On some methods of model selection for linear and logistic regression
Two essays in statistics: a prediction divergence criterion for model selection & wavelet variance based estimation of latent time series models
TLDR
A new criterion for model selection is presented which is shown to be particularly well suited in "sparse" settings which is believed to be common in many research fields and an alternative to maximum likelihood estimation is presented.
Uniform moment bounds of Fisher's information with applications to time series
TLDR
An asymptotic expression for the mean squared prediction error of the least squares predictor in autoregressive moving average models is obtained and provides a solid theoretical foundation for some model selection criteria.
The Focussed Information Criterion
TLDR
A focussed information criterion for model selection, the FIC, is proposed using an unbiased estimate of limiting risk, and a method which for given focus parameter estimates the precision of any submodel-based estimator is developed.
Issues in Model Selection, Minimax Estimation, and Censored Data Analysis
In this dissertation, we address several research problems in statistical inference. We obtain results in the following four directions: linear model selection, minimax estimation of linear
DIRECT AUTOREGRESSIVE PREDICTORS FOR MULTISTEP PREDICTION: ORDER SELECTION AND PERFORMANCE RELATIVE TO THE PLUG IN PREDICTORS
A direct method for multistep prediction of a stationary time series con- sists of fitting a new autoregression for each lead time, h, by a linear regression procedure and to select the order to be
SELECTION OF A MULTISTEP LINEAR PREDICTOR FOR SHORT TIME SERIES
We develop a version of the Corrected Akaike Information Criterion (AICC) suitable for selection of an h-step-ahead linear predictor for a weakly sta- tionary time series in discrete time. A
Selecting neural network architectures via the prediction risk: application to corporate bond rating prediction
  • J. Utans, J. Moody
  • Computer Science
    Proceedings First International Conference on Artificial Intelligence Applications on Wall Street
  • 1991
TLDR
The authors propose the prediction risk as a measure of the generalization ability of multi-layer perceptron networks and use it to select the optimal network architecture.
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