• Title, Summary, Keyword: Improper prior

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ARMA Model Identification Using the Bayes Factor

  • Son, Young-Sook
    • Journal of the Korean Statistical Society
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    • v.28 no.4
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    • pp.503-513
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    • 1999
  • The Bayes factor for the identification of stationary ARM(p,q) models is exactly computed using the Monte Carlo method. As priors are used the uniform prior for (\ulcorner,\ulcorner) in its stationarity-invertibility region, the Jefferys prior and the reference prior that are noninformative improper for ($\mu$,$\sigma$\ulcorner).

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Bayesian Analysis for Random Effects Binomial Regression

  • Kim, Dal-Ho;Kim, Eun-Young
    • Communications for Statistical Applications and Methods
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    • v.7 no.3
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    • pp.817-827
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    • 2000
  • In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

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A Bayesian Criterion for a Multiple test of Two Multivariate Normal Populations

  • Kim Hea-Jung;Son Young Sook
    • Proceedings of the Korean Statistical Society Conference
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    • pp.147-152
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    • 2000
  • A Bayesian criterion is proposed for a multiple test of two independent multivariate normal populations. For a Bayesian test the fractional Bayes facto.(FBF) of O'Hagan(1995) is used under the assumption of Jeffreys priors, noninformative improper proirs. In this test the FBF without the need of sampling minimal training samples is much simpler to use than the intrinsic Bayes facotr(IBF) of Berger and Pericchi(1996). Finally, a simulation study is performed to show the behaviors of the FBF.

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Bayesian Model Selection for Nonlinear Regression under Noninformative Prior

  • Na, Jonghwa;Kim, Jeongsuk
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.719-729
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    • 2003
  • We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

Jeffrey′s Noninformative Prior in Bayesian Conjoint Analysis

  • Oh, Man-Suk;Kim, Yura
    • Journal of the Korean Statistical Society
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    • v.29 no.2
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    • pp.137-153
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    • 2000
  • Conjoint analysis is a widely-used statistical technique for measuring relative importance that individual place on the product's attributes. Despsite its practical importance, the complexity of conjoint model makes it difficult to analyze. In this paper, w consider a Bayesian approach using Jeffrey's noninformative prior. We derive Jeffrey's prior and give a sufficient condition under which the posterior derived from the Jeffrey's prior is paper.

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Bayesian hypothesis testing for homogeneity of coecients of variation in k Normal populationsy

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.1
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    • pp.163-172
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    • 2010
  • In this paper, we deal with the problem for testing homogeneity of coecients of variation in several normal distributions. We propose Bayesian hypothesis testing procedures based on the Bayes factor under noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be dened 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 reference prior. Simulation study and a real data example are provided.

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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    • v.19 no.4
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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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The Fractional Bayes Factor Approach to the Bayesian Testing of the Weibull Shape Parameter

  • Cha, Young-Joon;Cho, Kil-Ho;Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.3
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    • pp.927-932
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    • 2006
  • The techniques for selecting and evaluating prior distributions are studied over recent years which the primary emphasis is on noninformative priors. But, noninformative priors are typically improper so that such priors are defined only up to arbitrary constants which affect the values of Bayes factors. In this paper, we consider the Bayesian hypotheses testing for the Weibull shape parameter based on fractional Bayes factor which is to remove the arbitrariness of improper priors. Also we present a numerical example to further illustrate our results.

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A Bayesian Criterion for a Multiple test of Two Multivariate Normal Populations

  • Kim, Hae-Jung;Son, Young-Sook
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.97-107
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    • 2001
  • A simultaneous test criterion for multiple hypotheses concerning comparison of two multivariate normal populations is considered by using the so called Bayes factor method. Fully parametric frequentist approach for the test is not available and thus Bayesian criterion is pursued using a Bayes factor that eliminates its arbitrariness problem induced by improper priors. Specifically, the fractional Bayes factor (FBF) by O'Hagan (1995) is used to derive the criterion. Necessary theories involved in the derivation an computation of the criterion are provided. Finally, an illustrative simulation study is given to show the properties of the criterion.

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Independent Testing in Marshall and Olkin's Bivariate Exponential Model Using Fractional Bayes Factor Under Bivariate Type I Censorship

  • Cho, Kil-Ho;Cho, Jang-Sik;Choi, Seung-Bae
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1391-1396
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    • 2008
  • In this paper, we consider two components system which the lifetimes have Marshall and Olkin's bivariate exponential model with bivariate type I censored data. We propose a Bayesian independent test procedure for above model using fractional Bayes factor method by O'Hagan based on improper prior distributions. And we compute the fractional Bayes factor and the posterior probabilities for the hypotheses, respectively. Also we select a hypothesis which has the largest posterior probability. Finally a numerical example is given to illustrate our Bayesian testing procedure.

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