Eva Cantoni

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Acknowledgments This present work, similar to a hike in the mountains of Switzerland, has taken time to achieve. This long-term project leaves some memories of people to whom I would like to express my gratitude. I would like to express my deepest gratitude to my supervisor, Professor Stephan Morgenthaler for his continuous encouragement, patience, guidance(More)
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(More)
Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for(More)
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