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Statistics | Mass Transportation Problems - Applications

Mass Transportation Problems

Applications

Rachev, Svetlozar T., Rüschendorf, Ludger

1998, XXVI, 430 p.

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  • About this book

This is the first comprehensive account of the theory of mass transportation problems and its applications. In volume I, the authors systematically develop the theory of mass transportation with emphasis to the Monge-Kantorovich mass transportation and the Kantorovich-Rubinstein mass transshipment problems, and their various extensions. They discuss a variety of different approaches towards solutions of these problems and exploit the rich interrelations to several mathematical sciences--from functional analysis to probability theory and mathematical economics. The second volume is devoted to applications to the mass transportation and mass transshipment problems to topics in applied probability, theory of moments and distributions with given marginals, queucing theory, risk theory of probability metrics and its applications to various fields, amoung them general limit theorems for Gaussian and non-Gaussian limiting laws, stochastic differential equations, stochastic algorithms and rounding problems. The book will be useful to graduate students and researchers in the fields of theoretical and applied probabilitry, operations research, computer science, and mathematical economics. The prerequisites for this book are graduate level probability theory and real and functional analysis.

Content Level » Research

Keywords » Maß - Moment - Probability theory - algorithms - functional analysis - operations research - queueing theory - stochastic differential equation - stochastic equation

Related subjects » Probability Theory and Stochastic Processes - Statistics

Table of contents 

Modifications of the Monge-Kantorovich Problems: Transportation Problems with Relaxed or Additional Constraints.- Application of Kantorovich-Type Metrics to Various Probabilistic-Type Limit Theorems.- Mass Transportation Problems and Recursive Stochastic Equations.- Stochastic Differential Equations and Empirical Measures.

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