E-Book, Englisch, 113 Seiten
Cherubini / Durante / Mulinacci Marshall Olkin Distributions - Advances in Theory and Applications
1. Auflage 2015
ISBN: 978-3-319-19039-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Bologna, Italy, October 2013
E-Book, Englisch, 113 Seiten
Reihe: Springer Proceedings in Mathematics & Statistics
ISBN: 978-3-319-19039-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book presents the latest advances in the theory and practice of Marshall-Olkin distributions. These distributions have been increasingly applied in statistical practice in recent years, as they make it possible to describe interesting features of stochastic models like non-exchangeability, tail dependencies and the presence of a singular component. The book presents cutting-edge contributions in this research area, with a particular emphasis on financial and economic applications. It is recommended for researchers working in applied probability and statistics, as well as for practitioners interested in the use of stochastic models in economics. This volume collects selected contributions from the conference “Marshall-Olkin Distributions: Advances in Theory and Applications,” held in Bologna on October 2-3, 2013.
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Research
Autoren/Hrsg.
Weitere Infos & Material
A Survey of Dynamic Representations and Generalizations of the Marshall–Olkin Distribution German Bernhart, Lexuri Fernández, Jan-Frederik Mai, Steffen Schenk , and Matthias Scherer.- Copulas Based on Marshall–Olkin Machinery by Fabrizio Durante and Stéphane Girard, and Gildas Mazo.- The mean of Marshall–Olkin dependent exponential random by Lexuri Fernández and Jan-Frederik Mai and Matthias Scherer.- General Marshall-Olkin Models, Dependence Orders and Comparisons of Environmental Processes by Esther Frostig and Franco Pellerey.- Marshall-Olkin Machinery and Power Mixing: the Mixed Generalized Marshall-Olkin Distribution by Sabrina Mulinacci.- Extended Marshall-Olkin Model and Its Dual Version by Jayme Pinto and Nikolai Kolev.