R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization
为指数族回归模型（包括泊松、逻辑斯蒂等）提出了一种基于Kullback-Leibler散度的R平方拟合优度度量，可解释为回归变量减少的不确定性比例，适用于医疗保健利用等计数数据分析。
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.(This abstract was borrowed from another version of this item.)