• 제목/요약/키워드: noninformative prior

검색결과 131건 처리시간 0.02초

Bayesian Prediction Inference for Censored Pareto Model

  • Ko, Jeong-Hwan;Kim, Young-Hoon
    • Journal of the Korean Data and Information Science Society
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    • 제10권1호
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    • pp.147-154
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    • 1999
  • Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

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Bayes Estimation of Two Ordered Exponential Means

  • Hong, Yeon-Woong;Kwon, Yong-Mann
    • Journal of the Korean Data and Information Science Society
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    • 제15권1호
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    • pp.273-284
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    • 2004
  • Bayes estimation of parameters is considered for two independent exponential distributions with ordered means. Order restricted Bayes estimators for means are obtained with respect to inverted gamma, noninformative prior and uniform prior distributions, and their asymptotic properties are established. It is shown that the maximum likelihood estimator, restricted maximum likelihood estimator, unrestricted Bayes estimator, and restricted Bayes estimator of the mean are all consistent and have the same limiting distribution. These estimators are compared with the corresponding unrestricted Bayes estimators by Monte Carlo simulation.

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Bayesian testing for the homogeneity of the shape parameters of several inverse Gaussian distributions

  • Lee, Woo Dong;Kim, Dal Ho;Kang, Sang Gil
    • Journal of the Korean Data and Information Science Society
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    • 제27권3호
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    • pp.835-844
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    • 2016
  • We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jereys' prior or the reference prior can be used. Jereys' prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

Bayesian Hypothesis Testing for the Ratio of Two Quantiles in Exponential Distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • 제18권3호
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    • pp.833-845
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    • 2007
  • When X and Y have independent exponential distributions, we develop a Bayesian testing procedure for the ratio of two quantiles under reference prior. The noninformative prior such as reference prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we develop a Bayesian testing procedure based on fractional Bayes factor and intrinsic Bayes factor. We show that the posterior density under the reference prior is proper and propose the Bayesian testing procedure for the ratio of two quantiles using fractional Bayes factor and intrinsic Bayes factor. Simulation study and a real data example are provided.

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An objective Bayesian analysis for multiple step stress accelerated life tests

  • Kim, Dal-Ho;Kang, Sang-Gil;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • 제20권3호
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    • pp.601-614
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    • 2009
  • This paper derives noninformative priors for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We nd the objective priors for this model and show that the reference prior satisfies first order matching criterion. Also, we show that there exists no second order matching prior. Some simulation results are given and using artificial data, we perform Bayesian analysis for proposed priors.

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Bayesian Inference for Switching Mean Models with ARMA Errors

  • Son, Young Sook;Kim, Seong W.;Cho, Sinsup
    • Communications for Statistical Applications and Methods
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    • 제10권3호
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    • pp.981-996
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    • 2003
  • Bayesian inference is considered for switching mean models with the ARMA errors. We use noninformative improper priors or uniform priors. The fractional Bayes factor of O'Hagan (1995) is used as the Bayesian tool for detecting the existence of a single change or multiple changes and the usual Bayes factor is used for identifying the orders of the ARMA error. Once the model is fully identified, the Gibbs sampler with the Metropolis-Hastings subchains is constructed to estimate parameters. Finally, we perform a simulation study to support theoretical results.

K개 지수분포의 상등에 관한 베이지안 다중검정 (Bayesian Testing for the Equality of K-Exponential Populations)

  • 문경애;김달호
    • Journal of the Korean Data and Information Science Society
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    • 제12권1호
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    • pp.41-50
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    • 2001
  • 독립이면서 지수분포를 따르는 K개 모집단의 평균차이에 대한 가설 검정방법으로 Beregr와 Perrichi (1996, 1998)가 제안한 내재적 베이즈 요인을 이용한 베이지안 방법을 제안한다. 이 때 모수에 대한 사전분포로는 무정보적 사전분포를 사용한다. 모의실험을 통하여 제안한 검정방법의 유용성을 알아본다.

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부분 베이즈요인을 이용한 로그정규분포의 상등에 관한 베이지안검정 (Bayesian Testing for the Equality of Two Lognormal Populations with the fractional Bayes factor)

  • 문경애;김달호
    • Journal of the Korean Data and Information Science Society
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    • 제12권1호
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    • pp.51-59
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    • 2001
  • 독립이면서 로그정규분포를 따르는 두 모집단의 평균 차이에 대한 검정으로 O'Hagan (1995)이 제안한 부분 베이즈요인을 이용한 베이지안 방법을 제안한다. 이 때 모수에 대한 사전분포로는 무정보적 사전분포를 사용한다. 제안한 검정 방법의 유용성을 알아보기 위하여 실제 자료의 분석과 모의실험을 이용하여 고전적인 검정방법과 그 결과를 비교한다.

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디폴트 베이즈인자를 이용한 포아송 평균모수에 대한 다중검정 (A Multiple Test of a Poisson Mean Parameter Using Default Bayes Factors)

  • 김경숙;손영숙
    • 품질경영학회지
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    • 제30권2호
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    • pp.118-129
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    • 2002
  • A multiple test of a mean parameter, λ, in the Poisson model is considered using the Bayes factor. Under noninformative improper priors, the intrinsic Bayes factor(IBF) of Berger and Pericchi(1996) and the fractional Bayes factor(FBF) of O'Hagan(1995) called as the default or automatic Bayes factors are used to select one among three models, M$_1$: λ< $λ_0, M$_2$: λ= $λ_0, M$_3$: λ> $λ_0. Posterior probability of each competitive model is computed using the default Bayes factors. Finally, theoretical results are applied to simulated data and real data.

Default Bayesian Method for Detecting the Changes in Sequences of Independent Exponential and Poisson Random Variates

  • Jeong, Su-Youn;Son, Young-Sook
    • Communications for Statistical Applications and Methods
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    • 제9권1호
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    • pp.129-139
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    • 2002
  • Default Bayesian method for detecting the changes in sequences of independent exponential random variates and independent Poisson random variates is considered. Noninformative priors are assumed for all the parameters in both of change models. Default Bayes factors, AIBF, MIBF, FBF, to check whether there is any change or not on each sequence and the posterior probability densities of change at each time point are derived. Theoretical results discussed in this paper are applied to some numerical data.