• Communications for Statistical Applications and Methods
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• 제29권5호
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• pp.547-559
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• 2022

For a chi-squared test, which is a statistical method used to test the independence of a contingency table of two factors, the expected frequency of each cell must be greater than 5. The percentage of cells with an expected frequency below 5 must be less than 20% of all cells. However, there are many cases in which the regional expected frequency is below 5 in general small area studies. Even in large-scale surveys, it is difficult to forecast the expected frequency to be greater than 5 when there is small area estimation with subgroup analysis. Another statistical method to test independence is to use the Bayes factor, but since there is a high ratio of data dependency due to the nature of the Bayesian approach, the low expected frequency tends to decrease the precision of the test results. To overcome these limitations, we will borrow information from areas with similar characteristics and pool the data statistically to propose a pooled Bayes test of independence in target areas. Jo et al. (2021) suggested hierarchical Bayesian pooling models for small area estimation of categorical data, and we will introduce the pooled Bayes factors calculated by expanding their restricted pooling model. We applied the pooled Bayes factors using bone mineral density and body mass index data from the Third National Health and Nutrition Examination Survey conducted in the United States and compared them with chi-squared tests often used in tests of independence.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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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.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.541-548
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• 2000

Various statistical methods for assessment of equivalence in average bioavailabilities have been developed under the assumption that the intra-subject variabilities for the test and reference formulations are the same. Without the assumption, assessing the equivalence in average bioavailabilites does not imply that the two formulations are therapeutically equivalent and exchangeable. The most commonly used test procedure for equality of variabilites in 2$\times$2 crossover experiment is the so called Pitman-Morgan's adjusted F test based on the model without carryover effects (Chow and Liu(1992)). In this paper, a Bayesian method based on the Intrinsic Bayes Factor is proposed, which can be applied to the model with carryover effects.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1999년도 춘계학술발표회요약집
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• pp.42-42
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• 1999

• Journal of the Korean Statistical Society
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• 제29권1호
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• pp.123-135
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• 2000

This paper addresses the Bayesian hypotheses testing for the comparison of exponential population under type II censoring. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic, expected and median intrinsic Bayes factors for our problem. The Monte Carlo simulation is used for calculating intrinsic Bayes factors which are compared with P-values of the classical test.

• 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.

• 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.

• 응용통계연구
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• 제15권1호
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• pp.139-151
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• 2002

본 논문에서는 베이지안 기법을 이용한 비선형회귀모형의 선택법을 제안하였다. 베이즈요인에 기초한 이 방법은 주로 대표본의 경우에 이용되는 고전적 모형선택법에 비해 사전정보를 이용하는 측면과 비내포모형 및 소표본의 경우에 대해서도 효과적으로 사용될 수 있다는 장점을 가진다. 본 논문에서는 정보적 사전분포를 고려하였으며, 베이즈요인의 추정 방법으로 Laplace - Metropolis 추정 법을 제안하였다. 또한 MCMC 과정을 통해 추정된 모수의 수렴진단에 대해서도 고려하였다. 실제자료에 대한 최적의 모형선택 및 진단과정을 구체적으로 제시하였다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.479-496
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• 2003

We investigate logit confidence intervals for the odds ratio based on the delta method. These intervals are constructed using pseudo-Bayes estimators. The Gart method and Agresti method smooth the observed counts toward the model of equiprobability and independence, respectively. We obtain better coverage probability by smoothing the observed counts toward the pseudo-Bayes estimators in 2$\times$2 table. We also improve legit confidence intervals in 2$\times$2$\times$K tables by generalizing these ideas. Utilizing pseudo-Bayes estimators, we obtain better coverage probability by smoothing the observed counts toward the conditional independence model, no three-factor interaction model and saturated model in 2$\times$2$\times$K tables.

• 품질경영학회지
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• 제28권3호
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• pp.1-10
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• 2000

In this paper, we address the Bayesian hypotheses testing for the comparison of Weibull distributions. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are Improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors for the comparison of Weibull lifetime model and we use these results to analyze real data sets.