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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 those of the (unobservable) function ''g''. It is an inverse measure of the [[explanatory power]] of <math>\widehat{g}.</math>
If the smoothing procedure has [[operator matrix]] ''L'', then :<math>\operatorname{MSPE}(L)=\operatorname{E}\left[\left( g(x_i)-\widehat{g}(x_i)\right)^2\right].</math>
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