Publication

An Improvement of Akaike's FPE Criterion to Reduce its Variability

1994
Reports

Abstract

The selection of the number of past observations to be included in a linear predictor (the order of the predictor) should be done with minimum variability, since this latter is not taken into account in the inference stage. For finite time series, there is a trade-off between variability and optimality (in the sense of mean squared prediction error). The widely used Akaike criteria, FPE and AIC, lead to highly variable estimated orders, whereas consistent criteria are downward biased when the optimal order increases with the sample size. We introduce a new automatic test procedure based on the FPE criterion which decreases the variability without biasing the selection by much.

Official source

https://infoscience.epfl.ch/entities/publication/a4e454fe-0a96-4ced-a9ff-2f846686011d

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1994
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a4e454fe-0a96-4ced-a9ff-2f846686011d

About this result

Related concepts (32)

Mean squared prediction error

In statistics the mean squared prediction error (MSPE), also known as mean squared error of the predictions, of a smoothing, curve fitting, or regression procedure is the expected value of the squared prediction errors (PE), the square difference between the fitted values implied by the predictive function and the values of the (unobservable) true value g. It is an inverse measure of the explanatory power of and can be used in the process of cross-validation of an estimated model.

Mean squared error

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

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An Improvement of Akaike's FPE Criterion to Reduce its Variability

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