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In control theory, the RMSE is used as a quality measure to evaluate the performance of a state observer. [ 10 ] In fluid dynamics , normalized root mean square deviation (NRMSD), coefficient of variation (CV), and percent RMS are used to quantify the uniformity of flow behavior such as velocity profile, temperature distribution, or gas species ...
and the RMS for a function over all time is = [()]. The RMS over all time of a periodic function is equal to the RMS of one period of the function. The RMS value of a continuous function or signal can be approximated by taking the RMS of a sample consisting of equally spaced observations.
The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
Typically RMSD is used as a quantitative measure of similarity between two or more protein structures. For example, the CASP protein structure prediction competition uses RMSD as one of its assessments of how well a submitted structure matches the known, target structure. Thus the lower RMSD, the better the model is in comparison to the target ...
For example, an arrow can be drawn from the point representing an older version of a model to a newer version, which makes it easier to indicate more clearly whether or not the model is moving toward "truth," as defined by observations.
When the model has been estimated over all available data with none held back, the MSPE of the model over the entire population of mostly unobserved data can be estimated as follows.
Compute the Euclidean or Mahalanobis distance from the query example to the labeled examples. Order the labeled examples by increasing distance. Find a heuristically optimal number k of nearest neighbors, based on RMSE. This is done using cross validation. Calculate an inverse distance weighted average with the k-nearest multivariate neighbors.
It is remarkable that the sum of squares of the residuals and the sample mean can be shown to be independent of each other, using, e.g. Basu's theorem.That fact, and the normal and chi-squared distributions given above form the basis of calculations involving the t-statistic: