• Communications for Statistical Applications and Methods
• /
• 제13권1호
• /
• pp.177-190
• /
• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

• 응용통계연구
• /
• 제25권6호
• /
• pp.999-1008
• /
• 2012

A Bayesian multiple change-point model for small data is proposed for multivariate means and is an extension of the univariate case of Cheon and Yu (2012). The proposed model requires data from a multivariate noncentral $t$-distribution and conjugate priors for the distributional parameters. We apply the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model to detecte multiple change-points. The performance of our proposed algorithm has been investigated on simulated and real dataset, Hanwoo fat content bivariate data.

• Journal of the Korean Statistical Society
• /
• 제33권2호
• /
• pp.169-189
• /
• 2004

Under the assumption of default priors, such as noninformative priors, Bayesian model determination and parameter estimation of regression models with stationary and invertible ARMA errors are developed under exact full likelihoods. The default Bayes factors, the fractional Bayes factor (FBF) of O'Hagan (1995) and the arithmetic intrinsic Bayes factors (AIBF) of Berger and Pericchi (1996a), are used as tools for the selection of the Bayesian model. Bayesian estimates are obtained by running the Metropolis-Hastings subchain in the Gibbs sampler. Finally, the results of numerical studies, designed to check the performance of the theoretical results discussed here, are presented.

• Communications for Statistical Applications and Methods
• /
• 제10권3호
• /
• pp.981-996
• /
• 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.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 2006년도 춘계학술대회
• /
• pp.319-324
• /
• 2006

In this paper, we propose Bayesian procedure for the multiple change points analysis in a sequence of fractions nonconforming. We first compute the Bayes factor for detecting the existence of no change, a single change or multiple changes. The Gibbs sampler with the Metropolis-Hastings subchain is run to estimate parameters of the change point model, once the number of change points is identified. Finally, we apply the results developed in this paper to both a real and simulated data.

• Communications for Statistical Applications and Methods
• /
• 제24권3호
• /
• pp.227-240
• /
• 2017

In this paper, we introduce the inverted exponentiated Weibull (IEW) distribution which contains exponentiated inverted Weibull distribution, inverse Weibull (IW) distribution, and inverted exponentiated distribution as submodels. The proposed distribution is obtained by the inverse form of the exponentiated Weibull distribution. In particular, we explain that the proposed distribution can be interpreted by Marshall and Olkin's book (Lifetime Distributions: Structure of Non-parametric, Semiparametric, and Parametric Families, 2007, Springer) idea. We derive the cumulative distribution function and hazard function and calculate expression for its moment. The hazard function of the IEW distribution can be decreasing, increasing or bathtub-shaped. The maximum likelihood estimation (MLE) is obtained. Then we show the existence and uniqueness of MLE. We can also obtain the Bayesian estimation by using the Gibbs sampler with the Metropolis-Hastings algorithm. We also give applications with a simulated data set and two real data set to show the flexibility of the IEW distribution. Finally, conclusions are mentioned.

• 응용통계연구
• /
• 제17권2호
• /
• pp.281-301
• /
• 2004

본 논문에서는 일변량 정규분포를 따르는 확률변수의 관측치열에 대한 변화점 문제(change point problem)를 고찰한다. 변화점의 존재유무, 그리고 만일 변화점이 존재한다면 어떠한 유형으로 발생했는지 즉, 변화점 발생 이후로 평균만 변화, 분산만 변화, 또는 평균과 분산 모두가 변화했는지를 밝힌다. 가능한 여러 유형의 변화모형들 가운데 최적의 모형을 선택하기 위해 베이지안 모형선택 기법을 이용하고, 선택된 모형에 내재된 모수를 추정 하기 위해 메트로폴리스-혜스팅스 알고리 즘을 포함한 깁스샘플링 을 이용한다. 이러한 방법론은 모의실험을 통해 검토되고, 또한 서울지역의 겨울철 평균기온 자료에 적용된다.

• 응용통계연구
• /
• 제35권1호
• /
• pp.131-146
• /
• 2022

로지스틱 회귀 모형은 다양한 분야에서 범주형 종속 변수를 예측하거나 분류하기 위한 모형으로 많이 사용되고 있다. 로지스틱 회귀 모형에 대한 전통적인 베이지안 추론 기법으로 메트로폴리스-헤이스팅스 알고리즘이 많이 사용되었지만, 수렴의 속도가 느리고 제안 분포에 대한 적절성을 보장하기 어렵다. 따라서, 본 논문에서는 모형에 대한 베이지안 추론 방법으로 Frühwirth-Schnatter와 Frühwirth (2007)에서 제안된 보조 혼합 샘플링(auxiliary mixture sampling) 기법을 사용하였다. 이 방법은 모형의 선형성과 정규성을 만족시키기 위해 두 단계에 거쳐 잠재변수를 도입하며, 결과적으로 깁스 샘플링을 통한 추론을 가능하게 한다. 제안한 모형의 효과를 검증하기 위해 2020년 지역사회 건강조사 당뇨병 자료에 적용하여 메트로폴리스-헤이스팅스를 사용한 모형과 추론 결과를 비교 분석하였다. 또한, 다양한 분류 모형들과 본 논문에서 제안한 모형의 분류 성능을 비교한 결과 제안된 모형이 분류 분석에서도 좋은 성능을 보이는 것을 확인할 수 있었다.

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

• Communications for Statistical Applications and Methods
• /
• 제20권6호
• /
• pp.427-438
• /
• 2013

Based on progressive Type II censored sampling which is an important method to obtain failure data in a lifetime study, we suggest a very general form of Bayesian prediction bounds from two parameters exponentiated Weibull distribution using the proper general prior density. For this, Markov chain Monte Carlo approach is considered and we also provide a simulation study.