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Adjusted R2 Measures for the Inverse Gaussian Regression Model

Author

Listed:
  • Harald Heinzl

    (University of Vienna)

  • Martina Mittlböck

    (University of Vienna)

Abstract

Summary The R2 measure is a commonly used tool for assessing the predictive ability of a linear regression model. It quantifies the amount of variation in the outcome variable, which is explained by the covariates. Various attempts have been made to carry the R2 definition to other types of regression models as well. Here, two different R2 measure definitions for the Inverse Gaussian regression model will be studied. They are motivated by deviance and sums-of-squares residuals. Depending on sample size and number of covariates fitted, these R2 measures may show substantially inflated values, and a proper bias-adjustment is necessary. Several possible adjusted R2 measure definitions for the Inverse Gaussian regression model will be compared in a simulation study. The use of adjusted R2 measures is recommended in general.

Suggested Citation

  • Harald Heinzl & Martina Mittlböck, 2002. "Adjusted R2 Measures for the Inverse Gaussian Regression Model," Computational Statistics, Springer, vol. 17(4), pages 525-544, December.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:4:d:10.1007_s001800200125
    DOI: 10.1007/s001800200125
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    References listed on IDEAS

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    1. 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 209-220, April.
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    Cited by:

    1. Ricci, Lila, 2010. "Adjusted R-squared type measure for exponential dispersion models," Statistics & Probability Letters, Elsevier, vol. 80(17-18), pages 1365-1368, September.

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