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A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures

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  • Eva Cantoni
  • Elvezio Ronchetti
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    Abstract

    In this paper robust statistical procedures are presented for the analysis of skewed and heavy-tailed outcomes as they typically occur in health care data. The new estimators and test statistics are extensions of classical maximum likelihood techniques for generalized linear models. In contrast to their classical counterparts, the new robust techniques show lower variability and excellent effciency properties in the presence of small deviations form the assumed model, i.e. when the underlying distribution of the data lies in a neighborhood of the model. A simulation study, an analysis on real data, and a sensitivity analysis confirm the good theoretical statistical properties of the new techniques.

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    Bibliographic Info

    Paper provided by Département des Sciences Économiques, Université de Genève in its series Research Papers by the Department of Economics, University of Geneva with number 2004.03.

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    Length: 18 pages
    Date of creation: May 2004
    Date of revision:
    Handle: RePEc:gen:geneem:2004.03

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    Related research

    Keywords: Deviations from the model; GLM modeling; health econometrics; heavy tails; robust estimation; robust inference;

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    1. Gilleskie, Donna B. & Mroz, Thomas A., 2004. "A flexible approach for estimating the effects of covariates on health expenditures," Journal of Health Economics, Elsevier, vol. 23(2), pages 391-418, March.
    2. Mullahy, John, 1997. "Heterogeneity, Excess Zeros, and the Structure of Count Data Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 337-50, May-June.
    3. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
    4. Manning, Willard G, et al, 1987. "Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment," American Economic Review, American Economic Association, vol. 77(3), pages 251-77, June.
    5. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
    6. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
    7. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    8. Jones, Andrew M., 2000. "Health econometrics," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 6, pages 265-344 Elsevier.
    9. Willard G. Manning & John Mullahy, 1999. "Estimating Log Models: To Transform or Not to Transform?," NBER Technical Working Papers 0246, National Bureau of Economic Research, Inc.
    10. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
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