M
Marco Scarsini
Researcher at Libera Università Internazionale degli Studi Sociali Guido Carli
Publications - 245
Citations - 4610
Marco Scarsini is an academic researcher from Libera Università Internazionale degli Studi Sociali Guido Carli. The author has contributed to research in topics: Random variable & Price of anarchy. The author has an hindex of 35, co-authored 237 publications receiving 4256 citations. Previous affiliations of Marco Scarsini include University of Pavia & Singapore University of Technology and Design.
Papers
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On measures of concordance
TL;DR: In this paper, the authors give a general definition of concordance and a set of axioms for concordances, and compare their results with known results in the discrete case.
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A note on stochastic dominance and inequality measures
Pietro Muliere,Marco Scarsini +1 more
TL;DR: In this paper, a sequence of inverse stochastic dominances is introduced on the set of distribution functions (of nonnegative random variables) and the partial orders are used to rank income distributions when Lorenz ordering does not hold.
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Archimedean copulae and positive dependence
Alfred Müller,Marco Scarsini +1 more
TL;DR: In this paper, the authors consider different issues related to Archimedean copulae and positive dependence, and investigate conditions for exchangeable binary sequences to admit an Archimmedean copula, and show that they depend on the extendibility of the sequence and therefore on its positive-dependence properties.
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Some Remarks on the Supermodular Order
Alfred Müller,Marco Scarsini +1 more
TL;DR: In this paper, the supermodular order is shown to be strictly stronger than the concordance order for dimension d = 3, and it is shown that it is closed with respect to weak convergence.
Posted Content
A note on stochastic dominance and inequality measures
Marco Scarsini,Pietro Muliere +1 more
TL;DR: In this article, a sequence of inverse stochastic dominances is introduced on the set of distribution functions (of nonnegative random variables) and the partial orders are used to rank income distributions when Lorenz ordering does not hold.