• 품질경영학회지
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• 제17권2호
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• pp.101-112
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• 1989

We investigate a procedure which detects the parameter change point in AR(1) by Bayesian Approach using Jeffrey prior, for example, coefficient change point, variance change point, coefficient and variance change point, etc. And we apply our procedure to the simulated data.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.241-246
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• 2000

A simple random sample is taken from a population and a particular survey item is subject to nonresponse that corresponds to random subsampling of the sampled values within adjustment cells. Our object is to estimate Bayesian probability interval of the population mean.

• Journal of the Korean Data and Information Science Society
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• 제6권2호
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• pp.29-39
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• 1995

A model used in the past may be altered at will in modeling for the future. For this situation, the multiprocess dynamic model provides a general framework. In this paper we consider the multiprocess discount normal model with parameters having a time dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.127-137
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• 2000

In this paper, we consider hierarchical Bayesian analysis for P(Y < X) using Gibbs sampler, where X and Y are independent normal distributions with unknown means and variances, respectively. Also numerical study using real data is provided.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1167-1176
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• 2005

Tweedie (1957a) proposed a method for the analysis of residuals from an inverse Gaussian population paralleling the analysis of variance in normal theory. He called it the analysis of reciprocals. In this paper, we propose a Bayesian model selection procedure based on the fractional Bayes factor for the analysis of reciprocals. Using the proposed model selection procedures, we compare with the classical tests.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
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• pp.430-435
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• 2004

Nonhomogeneous Poisson Process is often used to model failure times which occurred in software reliability and hardware reliability models. It can be characterized by its intensity functions or mean value functions. Many parametric intensity models have been proposed to account for the failure mechanism in real situation. In this paper, we propose a Bayesian semiparametric approach based on beta-mixtures. Two real datasets are analyzed.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.161-168
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• 1996

Multistage hierarchical models and Bayesian inferences about finite population total estimations are considered. Here, Gibbs sampling approach that can be used to predict the marginal posterior means needed for Bayesian inferences is proposed.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.731-740
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• 2003

Schervish(1996) and Lavine and Schervish(1999) have shown that the classical P-values and the Bayes factors fail to achieve the so-called coherence property, respectively. In this paper, we propose a new type of Bayesian P-value, namely the type LR Bayesian P-value, satisfying the coherence property. The proposed Bayesian P-values are very easy to use with since they are simple functions of likelihood ratio. Their performances are discussed and compared with those of other methods under several situations.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.141-150
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• 2006

Penalized likelihood regression for exponential families have been considered by Kim (2005) through smoothing parameter selection and asymptotically efficient low dimensional approximations. We derive approximate Bayesian confidence intervals based on Bayes model associated with lower dimensional approximations to provide interval estimates in penalized likelihood regression and conduct empirical studies to access their properties.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.205-208
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• 2005

We have fuzzy hypotheses testing from Bayesian statistics with ideas from fuzzy sets theory to generalize Bayesian methods both for samples of fuzzy data and for prior distributions with non-precise parameters. Appling the principle of agreement index, the posterior odds ratio in the favor of hypotheses $H_0$ is equal to product of the fuzzy odds ratio and the fuzzy likelihood ratio. If the Posterior odds ratio exceeds the grade judgement, we accept the hypothesis $H_0$ for the degree.