• Title/Summary/Keyword: 제프리스사전분포

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Bayesian Analysis for the Error Variance in a Two-Way Mixed-Effects ANOVA Model Using Noninformative Priors (무정보 사전분포를 이용한 이원배치 혼합효과 분산분석모형에서 오차분산에 대한 베이지안 분석)

  • 장인홍;김병휘
    • The Korean Journal of Applied Statistics
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    • v.15 no.2
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    • pp.405-414
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    • 2002
  • We consider the problem of estimating the error variance of in a two-way mixed-effects ANOVA model using noninformative priors. First, we derive Jeffreys' prior, a reference prior, and matching priors. We then provide marginal posterior distributions under those noninformative priors. Finally, we provide graphs of marginal posterior densities of the error variance and credible intervals for the error variance in two real data set and compare these credible intervals.

Reference Prior and Posterior in the AR(1) Model

  • Lee, Yoon-Jae
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.1
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    • pp.71-78
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    • 2005
  • Recently an important issue in Bayesian methodology is determination of noninformative prior distributions, often required when there is no idea of prior information. In this thesis attention is focused on the development of noninformative priors for stationary AR(1) model. The noninformative priors primarily discussed are the Jeffreys prior, and the reference priors. The remarkable points in the result are that the Jeffreys prior coincides with the reference prior for the case that $\rho$ is the parameter of interest.

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Noninformative Priors for the Ratio of Means of Two Poisson Distributions

  • Kang, Sang-Gil;Lee, Woo-Dong;Kim, Dal-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.2
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    • pp.201-207
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    • 2002
  • In this paper, Jeffrey's and reference priors are derived when the parameter of interest is the ratio of means of two in dependent Poisson distribution. To achieve the parameter orthogonality in the sense of Cox and Reid (1987), non-trivial orthogonal transformation is provided. The orthogonal transformation makes to find noninformative priors easy. Our simulation study indicates that the reference prior meet very well the target coverage probabilities in a frequentist sense. Using the real data, we compute Bayes estimator and MLE for the ratio of means based on the reference prior.

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