Root Mean Squared Deviation
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See: Mean Squared Error, Measure, Root Mean Squared Error, Root Mean Square, Least-Squares Estimation Algorithm.
References
2011
- http://en.wikipedia.org/wiki/Root_mean_square_deviation
- The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the difference s between values predicted by a model or an estimator and the values actually observed from the thing being modeled or estimated. RMSD is a good measure of accuracy. These individual differences are also called residuals, and the RMSD serves to aggregate them into a single measure of predictive power.
The RMSD of an estimator [math]\displaystyle{ \hat{\theta} }[/math] with respect to the estimated parameter [math]\displaystyle{ \theta }[/math] is defined as the square root of the mean square error: :[math]\displaystyle{ \operatorname{RMSD}(\hat{\theta}) = \sqrt{\operatorname{MSE}(\hat{\theta})} = \sqrt{\operatorname{E}((\hat{\theta}-\theta)^2)}. }[/math]
For an unbiased estimator, the RMSD is the square root of the variance, known as the standard error. .... In some disciplines, the RMSD is used to compare differences between two things that may vary, neither of which is accepted as the "standard".
- The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the difference s between values predicted by a model or an estimator and the values actually observed from the thing being modeled or estimated. RMSD is a good measure of accuracy. These individual differences are also called residuals, and the RMSD serves to aggregate them into a single measure of predictive power.