R-Squared Measures for Count Data Regression Models with Applications to Health Care Utilization
AbstractFor 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.
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Bibliographic InfoPaper provided by California Davis - Institute of Governmental Affairs in its series Papers with number 93-24.
Length: 23 pages
Date of creation: 1993
Date of revision:
Contact details of provider:
Postal: UNIVERSITY OF CALIFORNIA DAVIS, INSTITUTE OF GOVERNMENTAL AFFAIRS, RESEARCH PROGRAM IN APPLIED MACROECONOMICS AND MACRO POLICY, DAVIS CALIFORNIA 95616 U.S.A.
econometrics ; health;
Other versions of this item:
- Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-20, April.
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