Mean squared prediction error: Difference between revisions

Revision as of 13:49, 9 April 2012

Revision as of 22:52, 7 January 2013

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{{Unreferenced|date=December 2009}}

In [[statistics]] the '''mean squared prediction error''' of a [[smoothing]] or [[curve fitting]] procedure is the expected sum of squared deviations of the fitted values <math>\widehat{g}</math> from the (unobservable) function <math>g</math>. If the smoothing procedure has [[operator matrix]] <math>L</math>, then

:<math>\operatorname{MSPE}(L)=\operatorname{E}\left[\sum_{i=1}^n\left( g(x_i)-\widehat{g}(x_i)\right)^2\right].</math>