• Communications for Statistical Applications and Methods
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• 제21권2호
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• pp.147-160
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• 2014

In this paper, we introduce the exponentiated Weibull-geometric (EWG) distribution which generalizes two-parameter exponentiated Weibull (EW) distribution introduced by Mudholkar et al. (1995). This proposed distribution is obtained by compounding the exponentiated Weibull with geometric distribution. We derive its cumulative distribution function (CDF), hazard function and the density of the order statistics and calculate expressions for its moments and the moments of the order statistics. The hazard function of the EWG distribution can be decreasing, increasing or bathtub-shaped among others. Also, we give expressions for the Renyi and Shannon entropies. The maximum likelihood estimation is obtained by using EM-algorithm (Dempster et al., 1977; McLachlan and Krishnan, 1997). We can obtain the Bayesian estimation by using Gibbs sampler with Metropolis-Hastings algorithm. Also, we give application with real data set to show the flexibility of the EWG distribution. Finally, summary and discussion are mentioned.

• 응용통계연구
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• 제7권1호
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• pp.1-17
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• 1994

본 논문은 종속변수가 중도 절단된 경우 역추정을 베이지안 방법으로 접근하였는데 이때 특별히 Gibbs Sampling을 응용하여 사후분포를 계산하는 것을 토의하였다. 적용의 예로서 실제 자료에서의 점추정 및 Simulated Annealing을 이용한 구간추정도 하였다.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Journal of the Korean Statistical Society
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• 제28권4호
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• pp.443-455
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• 1999

A simulation-based approach to estimating the probability of an arbitrary region under a multivariate normal distribution is developed. In specific, the probability is expressed as the ratio of the unrestricted and the restricted multivariate normal density functions, where the restriction is given by the region whose probability is of interest. The density function of the restricted distribution is then estimated by using a sample generated from the Gibbs sampling algorithm.

• Journal of the Korean Statistical Society
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• 제31권4호
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• 품질경영학회지
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• 제26권1호
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• pp.88-98
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• 1998

Process capability indices are used to determine whether a production process is capable of producing items within a specified tolerance. Using conditional distribution, we study some process capability indices ${\hat{C}}_{Gp}$, ${\hat{C}}_{Gpk}$, ${\hat{C}}_{Gpm}$ under conjugate prior distribution. We consider some process capability indices with Gibbs sampling method. Also, we examine some small sample properties related to these estimaters by some simulations.

• 응용통계연구
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• 제26권5호
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• pp.713-723
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• 2013

본 연구에서는 2-모수 파레토분포에 대해 무정보사전분포인 준거사전분포의 가정 하에서 객관적 베이지안 모수추정 절차를 제안하였다. 베이지안 추정은 깁스샘플링에 의해서 수행된다. 깁스샘플러에서 모수생성하는 방법은 형태모수는 감마분포로부터 생성하고 척도모수는 적응기각표집 알고리즘에 의해 생성한다. 제안된 베이지안 모수추정 절차는 모의실험과 자료분석에서 기존의 추정방법들인 L-적률추정법, 최우추정법, 공액사전분포 하의 주관적 베이지안 모수추정법과 비교된다.

• 대한화학회지
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• 제51권4호
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• pp.312-317
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• 2007

난수 모의실험을 수행하여 격자 위에 분자를 무작위하게 배열하는 경우의 수의 분포를 다른 분자간의 최근린 상호작용수 N12에 대한 정규분포로 근사하였다. 이 분포로부터 논랜덤 혼합 격자용액의 과잉깁스 에너지 GE에 대한 근사식을 유도하였다. 이를 이용하여 여러 이성분용액의 액체-증기 상평형 계산을 하였고 기존의 식들의 계산 결과와 비교하여 보았다.

• 대한화학회지
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• 제53권6호
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• pp.663-670
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• 2009

논랜덤 혼합의 2-성분 격자용액에서 특정상호작용을 갖는 경우의 수에 대한 분포를 난수 모의실험을 통하여 구하였다. 이 분포로부터 2-성분격자용액의 과잉깁스에너지 $G^E$에 대한 근사식을 유도하였다. 이 식을 사용하여 15개의 2-성분용액에 대한 일정압력에서의 액체-증기 상평형 계산을 하였고 Wilson식, Van Laar식, Redlich-Kister식의 계산 결과와 비교하여 보았다.

• 한국안전학회지
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• 제36권2호
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• pp.32-38
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• 2021

This paper suggests an approach to evaluate the reliability of an intelligent power module with information deficiency of prior distribution and the characteristics of censored data through Bayesian statistics. This approach used a prior distribution of Bayesian statistics using the lifetime information provided by the manufacturer and compared and evaluated diffuse prior (vague prior) distributions. To overcome the computational complexity of Bayesian posterior distribution, it was computed with Gibbs sampling in the Monte Carlo simulation method. As a result, the standard deviation of the prior distribution developed using simple information was smaller than that of the posterior distribution calculated with the diffuse prior. In addition, it showed excellent error characteristics on RMSE compared with the Kaplan-Meier method.