Mean squared prediction error: Difference between revisions

Revision as of 16:08, 31 August 2016

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{{Unreferenced|date=December 2009}}

{{expert-subject|statistics|reason=no source, and notation/definition problems regarding ''L''}}

In [[statistics]] the '''mean squared prediction error''' of a [[smoothing]] or [[curve fitting]] procedure is the expected value of the squared difference between the fitted values implied by the predictive function <math>\widehat{g}</math> and the values of the (unobservable) function ''g''. It is an inverse measure of the [[explanatory power]] of <math>\widehat{g}.,</math> and can be used in the process of [[cross-validation (statistics)|cross-validation]] of an estimated model.

If the smoothing procedure has [[operator matrix]] ''L'', then