Abstract
A famous theorem of Birkhoff says that any doubly stochastic matrix D can be decomposed into a convex combination of permutation matrices R. The various decompositions correspond to probability distributions on the set of permutations that satisfy the linear constraints E[R] = D. This paper illustrates how to decompose D so that the resulting probability distribution is minimal in the sense that it does not majorize any other distribution satisfying these constraints.
Any distribution maximizing a strictly Schur concave function g under these linear constraints will be minimal in the above sense (Joe (1987)). In particular, for D in the relative interior of the convex hull of the permutation matrices, the probability functions p that maximize \(g\left( p \right)\, = \, - \,\sum\nolimits_\pi {p\left( \pi \right)} \,\) log p(π), subject to E[R] = D, form an exponential family £ with sufficient statistic R.
This paper provides a theorem that characterizes the exponential family £ by a property called quasi-independence. Quasi-independence is defined in terms of the invariance of the product measure over Latin sets. The characterization suggests an algorithm for an explicit minimal decomposition of a doubly stochastic matrix.
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© 1989 Springer-Verlag New York, Inc.
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Verducci, J.S. (1989). Minimum Majorization Decomposition. In: Gleser, L.J., Perlman, M.D., Press, S.J., Sampson, A.R. (eds) Contributions to Probability and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3678-8_12
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DOI: https://doi.org/10.1007/978-1-4612-3678-8_12
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