In statistics the mean squared prediction error of a smoothing procedure is the expected sum of squared deviations of the fitted values from the (unobservable) function . If the smoothing procedure has operator matrix, then
The MSPE can be decomposed into two terms (just like mean squared error is decomposed into bias and variance); however for MSPE one term is the sum of squared biases of the fitted values and another the sum of variances of the fitted values:
Note that knowledge of is required in order to calculate MSPE exactly.
Read more about Mean Squared Prediction Error: Estimation of MSPE
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