Cantoni, Eva Ronchetti, Elvezio 2004 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 efficiency properties in the presence of small deviations from 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. application/pdf https://susi.usi.ch/global/documents/317916 https://localhost:5000/ark:/12658/srd1317916 https://susi.usi.ch/documents/317916/files/ronchetti_JHE_2006.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jhealeco.2005.04.010 info:eu-repo/semantics/altIdentifier/ark/12658/srd1317916 info:eu-repo/semantics/openAccess License undefined Journal of Health Economics. - Elsevier Netherlands. - 2006, vol. 25, no. 2, p. 198-213 Deviations from the model GLM modeling health econometrics heavy tails robust estimation robust inference info:eu-repo/classification/udc/33 A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures http://purl.org/coar/resource_type/c_6501