A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures
AbstractIn 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 InfoPaper 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.
Length: 18 pages
Date of creation: May 2004
Date of revision:
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Deviations from the model; GLM modeling; health econometrics; heavy tails; robust estimation; robust inference;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- I10 - Health, Education, and Welfare - - Health - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-09-05 (All new papers)
- NEP-ECM-2004-09-05 (Econometrics)
- NEP-EDU-2004-09-05 (Education)
- NEP-HEA-2004-09-05 (Health Economics)
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