About: Mean squared prediction error

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In statistics the mean squared prediction error or mean squared error of the predictions of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values implied by the predictive function and the values of the (unobservable) function g. It is an inverse measure of the explanatory power of and can be used in the process of cross-validation of an estimated model. If the smoothing or fitting procedure has projection matrix (i.e., hat matrix) L, which maps the observed values vector to predicted values vector via then

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  • In statistics the mean squared prediction error or mean squared error of the predictions of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values implied by the predictive function and the values of the (unobservable) function g. It is an inverse measure of the explanatory power of and can be used in the process of cross-validation of an estimated model. If the smoothing or fitting procedure has projection matrix (i.e., hat matrix) L, which maps the observed values vector to predicted values vector via then The MSPE can be decomposed into two terms: the mean of squared biases of the fitted values and the mean of variances of the fitted values: Knowledge of g is required in order to calculate the MSPE exactly; otherwise, it can be estimated. (en)
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  • In statistics the mean squared prediction error or mean squared error of the predictions of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values implied by the predictive function and the values of the (unobservable) function g. It is an inverse measure of the explanatory power of and can be used in the process of cross-validation of an estimated model. If the smoothing or fitting procedure has projection matrix (i.e., hat matrix) L, which maps the observed values vector to predicted values vector via then (en)
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  • Mean squared prediction error (en)
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