About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Paper
A predictive least-squares principle
Abstract
A new principle of least-squares estimation is described, which extends the old in allowing the estimation of the number of the parameters along with their values. Just as the old principle, the new one too uses only a sum of squares, which now, however, represent prediction errors rather than fitting errors. In a typical regression problem with independent normal data, the estimates of the number of the parameters, i.e. the number of the regressor functions, are shown to be consistent. © 1986 Oxford University Press.