• Title/Summary/Keyword: Bayesian Testing

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Bayesian Hypothesis Testing for the Ratio of Exponential Means

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1387-1395
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    • 2006
  • This paper considers testing for the ratio of two exponential means. We propose a solution based on a Bayesian decision rule to this problem in which no subjective input is considered. The criterion for testing is the Bayesian reference criterion (Bernardo, 1999). We derive the Bayesian reference criterion for testing the ratio of two exponential means. Simulation study and a real data example are provided.

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Inferential Problems in Bayesian Logistic Regression Models (베이지안 로지스틱 회귀모형에서의 추론에 대한 연구)

  • Hwang, Jin-Soo;Kang, Sung-Chan
    • The Korean Journal of Applied Statistics
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    • v.24 no.6
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    • pp.1149-1160
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    • 2011
  • Model selection and hypothesis testing problems in Bayesian inference are still debated between scholars. Bayesian factors traditionally used as a criterion in Bayesian hypothesis testing and model selection, are easy to understand but sometimes hard to compute. In addition, there are other model selection criterions such as DIC(Deviance Information Criterion) by Spiegelhalter et al. (2002) and Bayesian P-values for testing. In this paper, we briefly introduce the Bayesian hypothesis testing and model selection procedure. In addition we have applied a Bayesian inference to Swiss banknote data by a fitting logistic regression model and computing several test statistics to see if they provide consistent results.

Bayesian Testing for Independence in Bivariate Exponential Model

  • Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.521-527
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    • 2006
  • In this paper, we consider the Bayesian hypotheses testing for independence in bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the fractional Bayes factor. Also we give some numerical results to illustrate our results.

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A Bayesian Hypothesis Testing Procedure Possessing the Concept of Significance Level

  • Hwang, Hyungtae
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.787-795
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    • 2001
  • In this paper, Bayesian hypothesis testing procedures are proposed under the non-informative prior distributions, which can be thought as the Bayesian counterparts of the classical ones in the sense of using the concept of significance level. The performances of proposed procedures are compared with those of classical procedures through several examples.

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Bayesian Testing for the Equality of Two Inverse Gaussian Populations with the Fractional Bayes Factor

  • Ko, Jeong-Hwan
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.3
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    • pp.539-547
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    • 2005
  • We propose the Bayesian testing for the equality of two independent Inverse Gaussian population means using the fractional Bayesian factors suggested by O' Hagan(1995). As prior distribution for the parameters, we assumed the noninformative priors. In order to investigate the usefulness of the proposed Bayesian testing procedures, the behaviors of the proposed results are examined via real data analysis.

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Bayesian Approach for Independence Test in Bivariate Exponential Model

  • Cho, Jang-Sik
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.04a
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    • pp.327-333
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    • 2006
  • In this paper, we consider the Bayesian hypotheses testing for independence in bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the fractional Bayes factor. Also we give some numerical results to illustrate our results.

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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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    • v.27 no.3
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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 Means in Exponential Distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.11a
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    • pp.205-213
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    • 2006
  • This paper considers testing for the ratio of two exponential means. We propose a solution based on a Bayesian decision rule to this problem in which no subjective input is considered. The criterion for testing is the Bayesian reference criterion (Bernardo, 1999). We derive the Bayesian reference criterion for testing the ratio of two exponential means. Simulation study and a real data example are provided.

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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.