• 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.

• International Journal of Reliability and Applications
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• 제14권2호
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• pp.97-105
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• 2013

A stress-strength model is formulated for a multi-component system consisting of k identical components. The k components of the system with random strengths ($X_1$, $X_2$, ${\ldots}$, $X_k$) are subjected to one of the r random stresses ($X_{k+1}$, $X_{k+2}$, ${\ldots}$, $X_{k+r}$). The estimation of system reliability based on maximum likelihood estimates (MLEs) and Bayes estimators in k component system are obtained when the system is either parallel or series with the assumption that strengths and stresses follow exponential distribution. A simulation study is conducted to compare MLE and Bayes estimator through the mean squared errors of the estimators.

• 한국의학물리학회지:의학물리
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• 제7권2호
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• pp.79-90
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• 1996

본 논문에서는 2 차원 심초음파도의 경계선 유사 영역에 대해 베이즈 추정기를 사용하여 경계선 검출을 위한 자동문턱 결정방법을 제안하고자 한다. 경계선 유사영역은 전처리과 정에서 흐려진 영상을 명확히 하는데 사용할 열비등방성 확산 방법의 전도계수로부터 얻어진다, 이러한 경계선 유사 영역에 대해 최적 문턱치를 선택하기 위해 베이즈 추정기가 사용되었다. 이 문턱치를 사용하여 영상을 이진화함으로서 심초음파도의 경계선을 자동 적으로 검출하게 된다. 마지막으로 본래의 심초음파도에 위에서 얻어진 경계선을 덧씌움으로써 경계선이 강조된 심초음파도를 얻을 수 있게 된다.

• 응용통계연구
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• 제7권1호
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• pp.159-172
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• 1994

여러개의 종으로 구성된 모집단으로부터 일정 크기의 표본을 추출한 경우 다음에 관측된 종이 신종일 확률에 대한 추정량으로 가장 널리 사용되어 온 것은 Good의 추정량이다. 본 논문에서는 종의 총 수효에 관한 사전정보가 존재할 경우 Good의 추정량에 대한 대안으로서 새로운 경험적 베이지안 추정량을 제안하였다. 모집단이 절단 기하분포를 따를 경우의 소표본 시뮬레이션 결과는 새로운 추정량의 편의가 별로 크지 않으며 RMSE가 Good의 추정량보다 작음을 보여 주었다.

• 품질경영학회지
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• 제22권2호
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• pp.69-78
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• 1994

We obtain the Bayes estimator(BE), the minimum variance unbiased estimator(MVUE) and maximun likelihood estimator(MLE) of the reliability when the distribution of the stress and the strength are Weibull with known shape parameters. The experiment is terminated before all of the items on the test have failed and the failed items are partially replaced. Performance of the three estimators for moderate size samples are compared through Monte Carlo simulation.

• International Journal of Reliability and Applications
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• 제18권2호
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• pp.83-98
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• 2017

Stress-strength reliability problems arise frequently in applied statistics and related fields. In the context of reliability, the stress-strength model describes the life of a component, which has a random strength X and is subjected to random stress Y. The component fails at the instant that the stress applied to it exceeds the strength and the component will function satisfactorily whenever X > Y. The problem of estimation the reliability parameter in a stress-strength model R = P[Y < X], when X and Y are two independent two-parameter Lindley random variables is considered in this paper. The maximum likelihood estimator (MLE) and Bayes estimator of R are obtained. Also, different confidence intervals of R are obtained. Simulation study is performed to compare the different proposed estimation methods. Example in real data is used as practical application of the proposed procedure.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제5권2호
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• pp.123-132
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• 1998

Let F and G denote the distribution functions of the failure times and the censoring variables in a random censorship model. Susarla and Van Ryzin(1978) verified consistency of $F_{\alpha}$, he NPBE of F with respect to the Dirichlet process prior D($\alpha$), in which they assumed F and G are continuous. Assuming that A, the cumulative hazard function, is distributed according to a beta process with parameters c, $\alpha$, Hjort(1990) obtained the Bayes estimator $A_{c,\alpha}$ of A under a squared error loss function. By the theory of product-integral developed by Gill and Johansen(1990), the Bayes estimator $F_{c,\alpha}$ is recovered from $A_{c,\alpha}$. Continuity assumption on F and G is removed in our proof of the consistency of $A_{c,\alpha}$ and $F_{c,\alpha}$. Our result extends Susarla and Van Ryzin(1978) since a particular transform of a beta process is a Dirichlet process and the class of beta processes forms a much larger class than the class of Dirichlet processes.

• Journal of the Korean Data and Information Science Society
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• 제8권2호
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• pp.261-270
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• 1997

This paper is proposed the parametric empirical Bayes(EB) confidence intervals which corrects the deficiencies in the naive EB confidence intervals of the scale parameter in the Weibull distribution under item-censoring scheme. In this case, the bootstrap EB confidence intervals are obtained by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive EB confidence intervals through Monte Carlo study are also presented.

• Journal of the Korean Statistical Society
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• 제30권3호
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• pp.481-498
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• 2001

Empirical Bayes procedure of nonparametric estiamtion of cumulative hazard rates based on censored data is considered using the beta process priors of Hjort(1990). Beta process priors with unknown parameters are used for cumulative hazard rates. Empirical Bayes estimators are suggested and asymptotic optimality is proved. Our result generalizes that of Susarla and Van Ryzin(1978) in the sensor that (i) the cumulative hazard rate induced by a Dirichlet process is a beta process, (ii) our empirical Bayes estimator does not depend on the censoring distribution while that of Susarla and Van Ryzin(1978) does, (iii) a class of estimators of the hyperprameters is suggested in the prior distribution which is assumed known in advance in Susarla and Van Ryzin(1978). This extension makes the proposed empirical Bayes procedure more applicable to real dta sets.

• 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.