Regression diagnostics | Regression variable selection

Mallows's Cp

In statistics, Mallows's Cp, named for Colin Lingwood Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares. It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the goal is to find the best model involving a subset of these predictors. A small value of Cp means that the model is relatively precise. Mallows's Cp has been shown to be equivalent to Akaike information criterion in the special case of Gaussian linear regression. (Wikipedia).

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Related pages

Dependent and independent variables | Collinearity | Coefficient of determination | Goodness of fit | Ordinary least squares | Linear regression | Regression analysis | Model selection | Feature selection | Akaike information criterion | Overfitting | Effect size | Statistics | Stepwise regression | Statistical population | Residual sum of squares