Probability Inequalities for n-Dimensional Rectangles via Multivariate Majorization

  • Y. L. Tong


Inequalities for the probability content \( P\left[ { \cap _{j\, = \,1}^n\left\{ {\,{a_{1j\,}}\, \leqslant \,{X_j}\, \leqslant \,{a_{2j}}} \right\}} \right] \) are obtained, via concepts of multivariate majorization (which involves the diversity of elements of the 2xn matrix A = (a ij)). A special case of the general result is that \( P\left[ { \cap _{j = 1}^n\left\{ {{a_{1j}} \leqslant {X_j} \leqslant {a_{2j}}} \right\}} \right] \leqslant P\left[ { \cap _{j = 1}^n\left\{ {{{\bar a}_1} \leqslant {X_j} \leqslant {{\bar a}_2}} \right\}} \right] \) for \( {\bar a_{i\,}} = \,\frac{1}{n}\,\sum\nolimits_{j = 1}^n {{a_{ij}}\,\left( {i\, = \,1,\,2} \right).} \). The main theorems apply in most important cases, including the exchangeable normal, t, chi-square and gamma, F, beta, and Dirichlet distributions. The proofs of the inequalities involve a convex combination of an n-dimensional rectangle and its permutation sets.


Convex Combination Stochastic Matrix Probability Inequality Weak Concept Chain Majorization 
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Copyright information

© Springer-Verlag New York, Inc. 1989

Authors and Affiliations

  • Y. L. Tong
    • 1
  1. 1.Georgia Institute of TechnologyUSA

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