• 제목/요약/키워드: Bayes

검색결과 924건 처리시간 0.03초

Component Steady-State Availabilty 의 Bayes 추정 (Bayes Estimation of Component Steady-State Availability)

  • 박춘일
    • 한국항해학회지
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    • 제17권1호
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    • pp.91-98
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    • 1993
  • This paper presents a class of Bayes estimation of component steady-state availability . Throughout this paper, we will denote the mean time between failure and the mean time between repair by MTBF and MTBR respectively. In section 2 , we investigated Bayes estimation of the steady-state availability for noninformative prior density function and in section 3, we compute Bayes estimation for conjugate prior density function.

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A Comparative Study on Bayes Estimators for the Multivariate Normal Mcan

  • Kim, Dal-Ho;Lee, In suk;Kim, Hyun-Sook
    • Communications for Statistical Applications and Methods
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    • 제6권2호
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    • pp.501-510
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    • 1999
  • In this paper, we consider a comparable study on three Bayes procedures for the multivariate normal mean estimation problem. In specific we consider hierarchical Bayes empirical Bayes and robust Bayes estimators for the normal means. Then three procedures are compared in terms of the four comparison criteria(i.e. Average Relative Bias (ARB) Average Squared Relative Bias (ASRB) Average Absolute Bias(AAB) Average Squared Deviation (ASD) using the real data set.

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Bayesian Model Selection in Weibull Populations

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • 제18권4호
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    • pp.1123-1134
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    • 2007
  • This article addresses the problem of testing whether the shape parameters in k independent Weibull populations are equal. We propose a Bayesian model selection procedure for equality of the shape parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedure based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real example are provided.

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Hierarchical Bayes Estimators of the Error Variance in Two-Way ANOVA Models

  • Chang, In Hong;Kim, Byung Hwee
    • Communications for Statistical Applications and Methods
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    • 제9권2호
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    • pp.315-324
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    • 2002
  • For estimating the error variance under the relative squared error loss in two-way analysis of variance models, we provide a class of hierarchical Bayes estimators and then derive a subclass of the hierarchical Bayes estimators, each member of which dominates the best multiple of the error sum of squares which is known to be minimax. We also identify a subclass of non-minimax hierarchical Bayes estimators.

A Bayes Criterion for Testing Homogeneity of Two Multivariate Normal Covariances

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제27권1호
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    • pp.11-23
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    • 1998
  • A Bayes criterion for testing the equality of covariance matrices of two multivariate normal distributions is proposed and studied. Development of the criterion invloves calculation of Bayes factor using the imaginary sample method introduced by Spiegelhalter and Smith (1982). The criterion is designed to develop a Bayesian test criterion, so that it provides an alternative test criterion to those based upon asymptotic sampling theory (such as Box's M test criterion). For the constructed criterion, numerical studies demonstrate routine application and give comparisons with the traditional test criteria.

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Optimal Convergence Rate of Empirical Bayes Tests for Uniform Distributions

  • Liang, Ta-Chen
    • Journal of the Korean Statistical Society
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    • 제31권1호
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    • pp.33-43
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    • 2002
  • The empirical Bayes linear loss two-action problem is studied. An empirical Bayes test $\delta$$_{n}$ $^{*}$ is proposed. It is shown that $\delta$$_{n}$ $^{*}$ is asymptotically optimal in the sense that its regret converges to zero at a rate $n^{-1}$ over a class of priors and the rate $n^{-1}$ is the optimal rate of convergence of empirical Bayes tests.sts.

Empirical Bayes Confidence Intervals of the Burr Type XII Failure Model

  • Choi, Dal-Woo
    • Journal of the Korean Data and Information Science Society
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    • 제10권1호
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    • pp.155-162
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    • 1999
  • This paper is concerned with the empirical Bayes estimation of one of the two shape parameters(${\theta}$) in the Burr(${\beta},\;{\theta}$) type XII failure model based on type-II censored data. We obtain the bootstrap empirical Bayes confidence intervals of ${\theta}$ by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive empirical Bayes confidence intervals through Monte Carlo study are also presented.

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Bayesian Hypothesis Testing for the Difference of Quantiles in Exponential Models

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • 제19권4호
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    • pp.1379-1390
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    • 2008
  • This article deals with the problem of testing the difference of quantiles in exponential distributions. We propose Bayesian hypothesis testing procedures for the difference of two quantiles under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the matching prior. Simulation study and a real data example are provided.

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Bayesian small area estimations with measurement errors

  • Goo, You Mee;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • 제24권4호
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    • pp.885-893
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    • 2013
  • This paper considers Bayes estimations of the small area means under Fay-Herriot model with measurement errors. We provide empirical Bayes predictors of small area means with the corresponding jackknifed mean squared prediction errors. Also we obtain hierarchical Bayes predictors and the corresponding posterior standard deviations using Gibbs sampling. Numerical studies are provided to illustrate our methods and compare their eciencies.