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Explained Residual Variation, Explained Risk, and Goodness of Fit
Edward L. Korn and Richard Simon
The American Statistician
Vol. 45, No. 3 (Aug., 1991), pp. 201-206
Stable URL: http://www.jstor.org/stable/2684290
Page Count: 6
You can always find the topics here!Topics: Regression analysis, Linear regression, Goodness of fit, Statistical models, Entropy, Logistic regression, Consistent estimators, Statistical estimation, Maximum likelihood estimation, Datasets
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A loss function approach is used to define the concepts of explained residual variation and explained risk for general regression models. Explained risk measures the ability of the covariates in a correctly specified model to distinguish differing outcomes. Explained residual variation, which is R2 for a linear model, estimates the explained risk with a penalty for poorly fitting models. Application of the general definitions to linear regression, logistic regression, and survival analysis is given. The importance of distinguishing the concepts of explained residual variation, explained risk, and goodness of fit is discussed.
The American Statistician © 1991 American Statistical Association