^Roger Powell,Janet Hergt, Jon Woodhead 2002. Improving isochron calculations with robust statistics and thebootstrap. Chemical Geology 185, 191–204. ^Linacre, J. M. (2002)."What do Infit and Outfit, Mean-square and Standardized mean?".Rasch Measurement Transactions.16(2): 878. 拓展阅读 ...
是均方根值(Root Mean Square Value)的缩写。 spellmanhv.cn In physical meaning, the RMS (Root-Mean-Square) is offten called the effective value or DC-equivalent value of a current [...] token.com.tw 在物理意义上,RMS (Root-Mean-Square) 是常被称为有 效值,或 DC-等效值电流,是...
In statistics, the mean square error or MSE of an estimator is one of many ways to quantify the difference between an estimator and the true value of the quantity being estimated. MSE is a risk function, corresponding to the expected value of the squared error loss or quadratic loss. MSE ...
Statistics at square one. II--Mean from frequency distribution.http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1640324doi:10.1136/bmj.1.6021.1325SwinscowT DBritish Medical JournalSwinscow, T. D. V. 1976 . II--Mean from frequency distribution. Br. Med. J. 1:1325-1326. ....
Defining Root Mean Square (RMS) In the realm of statistics, the Root Mean Square (RMS) is the square root of the average of the squares of a set of values. Also known as the quadratic mean, RMS is a specialized form of the generalized mean, with an exponent of 2. RMS can also be...
In this section, I evaluate the behaviors of three sets of the predicted values for the response variable, derived from the least square means, the BLUPs, and a linear mixed model with the random intercepts assumed to have a log-gamma distribution, respectively. Because a large sample size ...
In general, the quadratic mean of n numbers x1, x2,…, xn is the square root of the arithmetic mean of their squares, The arithmetic mean gives no indication of how widely the data are spread or dispersed about the mean. Measures of the dispersion are provided by the arithmetic and ...
Root Mean Square Error(RMSE) is thestandard deviationof theresiduals(prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around th...
A Test of the Mean Square Error Criterion for Restrictions in Linear Regression The objectives of this paper are to examine the mean square error criterion for rejecting or adopting restrictions on the parameter space in a regression m... Carlos,Toro-Vizcarrondo,T.,... - 《Journal of the Ame...
If you take the square root of the MSE, you obtain theroot mean square error (RMSE), which does use the natural data units. In other words, MSE is analogous to thevariance, whereas RMSE is akin to thestandard deviation.