• 제목/요약/키워드: 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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    • 제17권4호
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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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베이지안 로지스틱 회귀모형에서의 추론에 대한 연구 (Inferential Problems in Bayesian Logistic Regression Models)

  • 황진수;강성찬
    • 응용통계연구
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    • 제24권6호
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    • pp.1149-1160
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    • 2011
  • 기존의 frequentist 추론에 비해 Bayesian 추론에서의 가설 검정 및 모형 선택 문제는 학자들 간에 일치된 견해를 보이지 못하고 있으며 아직도 논란이 되는 것들이 많다. Bayesian 추론에서 가설 검정 및 모형 선택의 기준으로 널리 쓰이는 Bayes factor는 이해하기 쉬우나 여러 경우에 구하기 어려운 단점이 존재한다. 그 외에 다른 기준으로 Spiegelhalter 등 (2002)가 제시한 DIC(Deviance Information Criterion)과 frequentist 추론에서의 P-value에 대비되는 Bayesian P-value가 있다. 본 논문에서는 Swiss banknote 자료를 Bayesian 로지스틱 회귀모형으로 분석하고 관련 기준들을 구하여 각 기준들이 일관성 있는 결론을 보이는지 확인하고자 한다.

Bayesian Testing for Independence in Bivariate Exponential Model

  • Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • 제17권2호
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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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    • 제8권3호
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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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A Bayesian Approach to Fuzzy Hypotheses Testing with Revision of possibility distribution

  • 강만기
    • 한국전산응용수학회:학술대회논문집
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    • 한국전산응용수학회 2003년도 KSCAM 학술발표회 프로그램 및 초록집
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    • pp.13.2-13
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    • 2003
  • We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses versus on with loss function.

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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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    • 제16권3호
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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

  • 조장식
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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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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    • 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 Means in Exponential Distributions

  • 강상길;김달호;이우동
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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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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    • 제21권1호
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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.