… For normally distributed data, mean squared error (MSE) is … This apparent weakness has led
to a myriad of specialized error … to errors (substituting ϵ for x in Equation 12), squaring these …

… It is also memoryless—the squared error can be evaluated at each sample, independent of
… When a squared error function is combined with Gaussian assumptions on the source and …

We consider the problem of estimating an unknown but structured signal x 0 from its noisy
linear observations y = Ax 0 + z ∈ ℝ m . To the structure of x 0 is associated a structure …

… In this paper, we consider mean squared errors (MSE) of empirical predictors under a
general setup, where ML or REML estimators are used for the second stage. We obtain second-…

An exact and easily computable expression for the mean integrated squared error (MISE)
for the kernel estimator of a general normal mixture density, is given for Gaussian kernels of …

… To see how accurate PSE is, we compare the mean-squared error of PSE to the mean-squared
error of the unbiased estimator FPE. The meansquared error of FPE has a variance term …

… a linear regression model: least squares, principal components, … offer improvement in mean
squared error over least squares for a … of a smaller mean squared error than either of these. …

… The analysis in this paper gives geometric insights into the source of error in subband-based
system identification. In particular, the mean-squared error arises as the residual after an …

… ), are likely to yield unacceptably large standard errors because of small sample size in the
… Second-order approximation to the mean squared error (MSE) of the two-stage estimator …

… forecast mean squared errors is equivalent to equality of forecast error … ; that is, although
the generated error series were white noise, this … We took expected squared error as the 2 …