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Minimum distance estimation of the distribution functions of stochastically ordered random variables


  • Ronald E. Gangnon
  • William N. King


Stochastic ordering of distributions can be a natural and minimal restriction in an estimation problem. Such restrictions occur naturally in several settings in medical research. The standard estimator in such settings is the nonparametric maximum likelihood estimator (NPMLE). The NPMLE is known to be biased, and, even when the empirical cumulative distribution functions nearly satisfy the stochastic orderings, the NPMLE and the empirical cumulative distribution functions may differ substantially. In many settings, this can make the NPMLE seem to be an unappealing estimator. As an alternative to the NPMLE, we propose a minimum distance estimator of distribution functions subject to stochastic ordering constraints. Consistency of the minimum distance estimator is proved, and superior performance is demonstrated through a simulation study. We demonstrate the use of the methodology to assess the reproducibility of gradings of nuclear sclerosis from fundus photographs. Copyright 2002 Royal Statistical Society.

Suggested Citation

  • Ronald E. Gangnon & William N. King, 2002. "Minimum distance estimation of the distribution functions of stochastically ordered random variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 485-492.
  • Handle: RePEc:bla:jorssc:v:51:y:2002:i:4:p:485-492

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    Cited by:

    1. Darinka Dentcheva & Andrzej Ruszczynski, 2004. "Optimization Under First Order Stochastic Dominance Constraints," GE, Growth, Math methods 0403002, EconWPA, revised 07 Aug 2005.
    2. Karabatsos, George & Walker, Stephen G., 2007. "Bayesian nonparametric inference of stochastically ordered distributions, with PĆ³lya trees and Bernstein polynomials," Statistics & Probability Letters, Elsevier, vol. 77(9), pages 907-913, May.
    3. Darinka Dentcheva & Andrzej Ruszczynski, 2004. "Convexification of Stochastic Ordering," GE, Growth, Math methods 0402005, EconWPA, revised 05 Aug 2005.

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