An Improved Goodness of Fit Statistic for Probability Prediction Models
Joseph G. Pigeon, Joseph F. Heyse
Abstract
Joseph G. Pigeon, Joseph F. Heyse
Abstract
We consider the general case of probability prediction models having two or more outcomes and propose an adjusted χ2 statistic which can be used to assess the goodness of fit of these models. We present a simulation study to show that our proposed statistic has an approximate χ2 distribution under the null hypothesis. Two applications are provided to illustrate the use of the new statistic. The first application examines the fit of a logistic regression model using both the proposed statistic and the popular Hosmer-Lemeshow statistic and we compare and contrast these two methods. The second application evaluates the goodness of fit of a polychotomous regression model.
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We consider the general case of probability prediction models having two or more outcomes and propose an adjusted χ2 statistic which can be used to assess the goodness of fit of these models. We present a simulation study to show that our proposed statistic has an approximate χ2 distribution under the null hypothesis. Two applications are provided to illustrate the use of the new statistic. The first application examines the fit of a logistic regression model using both the proposed statistic and the popular Hosmer-Lemeshow statistic and we compare and contrast these two methods. The second application evaluates the goodness of fit of a polychotomous regression model.
Key concepts: Goodness of fit, Statistic, Statistics, Logistic regression, PRESS statistic, Ancillary statistic, Mathematics, Contrast (vision)