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Optimal couplings are totally positive and more

Published online by Cambridge University Press:  14 July 2016

Paul Glasserman
Affiliation:
Graduate School of Business, Columbia University, New York, NY 10027, USA. Email address: pg20@columbia.edu
David D. Yao
Affiliation:
Department of Industrial Engineering and Operations Research, Columbia University, 500 West 120th Street, New York, NY 10027-6699, USA. Email address: yao@columbia.edu

Abstract

An optimal coupling is a bivariate distribution with specified marginals achieving maximal correlation. We show that optimal couplings are totally positive and, in fact, satisfy a strictly stronger condition we call the nonintersection property. For discrete distributions we illustrate the equivalence between optimal coupling and a certain transportation problem. Specifically, the optimal solutions of greedily-solvable transportation problems are totally positive, and even nonintersecting, through a rearrangement of matrix entries that results in a Monge sequence. In coupling continuous random variables or random vectors, we exploit a characterization of optimal couplings in terms of subgradients of a closed convex function to establish a generalization of the nonintersection property. We argue that nonintersection is not only stronger than total positivity, it is the more natural concept for the singular distributions that arise in coupling continuous random variables.

Type
Part 6. Stochastic processes
Copyright
Copyright © Applied Probability Trust 2004 

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References

[1] Alon, N., Cosares, S., Hochbaum, D. S. and Shamir, R. (1989). An algorithm for the detection and construction of Monge sequences. Linear Algebra Appl. 114/115, 669680.CrossRefGoogle Scholar
[2] Barlow, R. E. and Proschan, F. (1975). Statistical Theory of Reliability and Life Testing. Holt, Rinehart and Winston, New York.Google Scholar
[3] Burkard, R. E., Klinz, B. and Rudolf, R. (1995). Perspectives of Monge properties in optimization. Tech. Rep., Institute for Mathematics B, Technical University Graz.Google Scholar
[4] Dietrich, B. (1990). Monge sequences, antimatroids, and the transportation problem with forbidden arcs. Linear Algebra Appl. 139, 133145.CrossRefGoogle Scholar
[5] Glasserman, P. and Yao, D. D. (1992). Generalized semi-Markov processes: antimatroid structure and second-order properties. Math. Operat. Res. 17, 444469.CrossRefGoogle Scholar
[6] Glasserman, P. and Yao, D. D. (1992). Some guidelines and guarantees for common random numbers. Manag. Sci. 38, 884908.Google Scholar
[7] Glasserman, P. and Yao, D. D. (1994). Monotone Structure in Discrete-Event Systems. John Wiley, New York.Google Scholar
[8] Hoffman, A. J. (1963). On simple linear programming problems. In Convexity (Proc. Symp. Pure Math. 7), ed. Klee, V., American Mathematical Society, Providence, RI, pp. 317327.Google Scholar
[9] Karlin, S. (1963). Total positivity and convexity preserving transformations. In Convexity (Proc. Symp. Pure Math. 7), ed. Klee, V., American Mathematical Society, Providence, RI, pp. 328352.Google Scholar
[10] Karlin, S. (1968). Total Positivity. Stanford University Press.Google Scholar
[11] Keilson, J. and Kester, A. (1978). Unimodality preservation in Markov chains. Stoch. Process. Appl. 7, 179190.Google Scholar
[12] Knott, M. and Smith, C. S. (1984). On the optimal mapping of distributions. J. Optimization Theory Appl. 43, 3949.Google Scholar
[13] Lorentz, G. G. (1953). An inequality for rearrangements. Amer. Math. Monthly 60, 176179.Google Scholar
[14] Rachev, S. T. (1984). The Monge-Kantorovich mass transference problem and its stochastic applications. Theory Prob. Appl. 29, 647676.Google Scholar
[15] Rachev, S. T. and Rüschendorf, L. (1998). Mass Transportation Problems , Vol. 1, Theory. Springer, New York.Google Scholar
[16] Rockafellar, R. T. (1970). Convex Analysis. Princeton University Press.CrossRefGoogle Scholar
[17] Ross, S. M. (1983). Stochastic Processes. John Wiley, New York.Google Scholar
[18] Rüschendorf, L. and Rachev, S. T. (1990). A characterization of random variables with minimum L2-distance. J. Multivariate Anal. 32, 4854.Google Scholar
[19] Shamir, R. and Dietrich, B. (1990). Characterization and algorithms for greedily solvable transportation problems. In Proc. First ACM-SIAM Symp. Discrete Algorithms , pp. 358366.Google Scholar
[20] Smith, C. S. and Knott, M. (1987). Note on the optimal transportation of distributions. J. Optimization Theory Appl. 52, 323329.Google Scholar
[21] Whitt, W. (1976). Bivariate distributions with given marginals. Ann. Math. Statist. 4, 12801289.Google Scholar