mean-square error


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mean-square error

[¦mēn ¦skwer ′er·ər]
(statistics)
The residual or error sum of squares divided by the number of degrees of freedom of the sum; gives an estimate of the error or residual variance.
References in periodicals archive ?
It means that if, for instance, there is a long series of missing data at one or more stations in the validation period and in the calibration period we have another gap pattern, then the general mean-square error will be shifted to the value at one of stations.
The difference between the mean-square error estimated at the validation period, which equals to 2.
n] are unavailable is based on the use of linearly constrained minimum mean-square error methods.
2] square and a lower regression mean-square error, it would typically be considered to be the superior model based on goodness-of-fit criteria in the estimation sample.
The most common difference measure is the mean-square error (MSE).
To estimate entire image mean-square error (MSE) is used
This approximation is reasonable when the excess mean-square error (EMSE) variance is less than 10% of the minimum mean--square error (MSE) variance [1-2].
The excess mean-square error of the adaptive weights updating algorithm, caused by the fluctuation in the weight coefficients, can be defined as: