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Correction to "Reference Ranges and Z-Score Equations for 19 Fetal Cardiac Biometry Structures From 18 to 34 Weeks' Gestation". [PDF]

J Ultrasound Med
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Discovery of polynomial equations for regression

Advances in Methodology and Statistics, 2004
Both equation discovery and regression methods aim at inducing models of numerical data. While the equation discovery methods are usually evaluated in terms of comprehensibility of the induced model, the emphasis of the regression methods evaluation is on their predictive accuracy.
Džeroski, Sašo, Ljubič, Peter, Todorovski, Ljupčo   +2 more
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Multiple Regression for Systems of Equations

Econometrica, 1946
Not ...
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Is the Regression Equation Adequate?—A Generalization

Technometrics, 1978
Box and Wetz [1] and more recently Suich and Derringer [4] have presented a procedure for determining the adequacy of the regression model. This procedure is extended to allow the experimenter to check the adequacy of a subset of variables in the regression model. An example is presented that demonstrates the use of the procedure.
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Systems of Regression Equations

2003
The previous chapters dealt with models for univariate financial time series. In many applications, it is desirable to model the joint behavior of multiple time series because of possible efficiency gains to the joint estimation of a system of time series models.
Eric Zivot, Jiahui Wang
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ESTIMATION FOR SEEMINGLY UNRELATED REGRESSION EQUATIONS

Statistics & Risk Modeling, 1997
Summary: The problem of estimating regression coefficients in seemingly unrelated regression equations under some quadratic losses and matrix losses is discussed. The necessary and sufficient conditions for the existence of the uniformly minimum risk equivariant estimator under an affine group and a transitive group of transformations are given ...
Fang, Kai-Tai, Lam, Peter C. B., Wu, Qi-Guang   +2 more
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Comparison of a class of regression equations

American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 1984
The method described in this paper offers a means of comparing linear regression equations with many parameters by utilizing joint parameter confidence regions. It is useful when comparing sets of data in which each set is represented by a similar regression equation.
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