• Title/Summary/Keyword: uniform stochastic ordering

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Statistical Inference Concerning Local Dependence between Two Multinomial Populations

  • Oh, Myong-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.413-428
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    • 2003
  • If a restriction is imposed only to a (proper) subset of parameters of interest, we call it a local restriction. Statistical inference under a local restriction in multinomial setting is studied. The maximum likelihood estimation under a local restriction and likelihood ratio tests for and against a local restriction are discussed. A real data is analyzed for illustrative purpose.

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Bayesian Estimation of Uniformly Stochastically Ordered Distributions with Square Loss

  • Oh, Myong-Sik
    • Communications for Statistical Applications and Methods
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    • v.18 no.3
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    • pp.295-300
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    • 2011
  • The Bayesian nonparametric estimation of two uniformly stochastically ordered distributions is studied. We propose a restricted Dirichlet Process. Among many types of restriction we consider only uniformly stochastic ordering in this paper since the computation of integrals is relatively easy. An explicit expression of the posterior distribution is given. When square loss function is used the posterior distribution can be obtained by easy integration using some computer program such as Mathematica.

Tests For and Against a Positive Dependence Restriction in Two-Way Ordered Contingency Tables

  • Oh, Myongsik
    • Journal of the Korean Statistical Society
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    • v.27 no.2
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    • pp.205-220
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    • 1998
  • Dependence concepts for ordered two-way contingency tables have been of considerable interest. We consider a dependence concept which is less restrictive than likelihood ratio dependence and more restrictive than regression dependence. Maximum likelihood estimation of cell probability under this dependence restriction is studied. The likelihood ratio statistics for and against this dependence are proposed and their large sample distributions are derived. A real data is analyzed to illustrate the estimation and testing procedures.

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