R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization

A. Cameron and Frank Windmeijer

Journal of Business & Economic Statistics, 1996, vol. 14, issue 2, 209-20

Abstract: For regression models other than the linear model, R-squared type goodness-to-fit summary statistics have been constructed for particular models using a variety of methods. The authors propose an R-squared measure of goodness of fit for the class of exponential family regression models, which includes logit, probit, Poisson, geometric, gamma, and exponential. This R-squared is defined as the proportionate reduction in uncertainty, measured by Kullback-Leibler divergence, due to the inclusion of regressors. Under further conditions concerning the conditional mean function, it can also be interpreted as the fraction of uncertainty explained by the fitted model.

Date: 1996
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Working Paper: R-Squared Measures for Count Data Regression Models with Applications to Health Care Utilization (1993)
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