• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1547-1555
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• 2015

We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.877-885
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• 2006

Recently it is proposed that the Bayesian networks used as conversational agent for topic inference is useful but the Bayesian networks require much time to model, and the Bayesian networks also have to be modified when the scripts, the database for conversation, are added or modified and this hinders the scalability of the agent. This paper presents a method to improve the scalability of the agent by constructing the Bayesian network from scripts automatically. The proposed method is to model the structure of Bayesian networks hierarchically and to utilize Noisy-OR gate to form the conditional probability distribution table (CPT). Experimental results with ten subjects confirm the usefulness of the proposed method.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.885-893
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• 2013

This paper considers Bayes estimations of the small area means under Fay-Herriot model with measurement errors. We provide empirical Bayes predictors of small area means with the corresponding jackknifed mean squared prediction errors. Also we obtain hierarchical Bayes predictors and the corresponding posterior standard deviations using Gibbs sampling. Numerical studies are provided to illustrate our methods and compare their eciencies.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.781-790
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• 1999

In this paper we consider estimation of cancer incidence rates for local areas. The raw estimates usually are based on small sample sizes and hence are usually unreliable. A hierarchical Bayes generalized linear model is used which connects the local areas thereby enabling one to 'borrow strength' Random effects with pairwise difference priors model the spatial structure in the data. The methods are applied to cancer incidence estimation for census tracts in a certain region of the state of New York.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.587-593
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• 2012

We study Bayesian estimates for finite population proportions in multinomial problems. To do this, we consider a three-stage hierarchical Bayesian model. For prior, we use Dirichlet density to model each cell probability in each cluster. Our method does not require complicated computation such as Metropolis-Hastings algorithm to draw samples from each density of parameters. We draw samples using Gibbs sampler with grid method. We apply this algorithm to a couple of simulation data under three scenarios and we estimate the finite population proportions using two kinds of approaches We compare results with the point estimates of finite population proportions and their standard deviations. Finally, we check the consistency of computation using differen samples drawn from distinct iterates.

• Journal of Korea Water Resources Association
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• v.53 no.5
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• pp.383-393
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• 2020

The main objective of this study is to provide a robust model for estimating parameters of the Clark unit hydrograph (UH) using the observed rainfall-runoff data in the Soyangang dam basin. In general, HEC-1 and HEC-HMS models, developed by the Hydrologic Engineering Center, have been widely used to optimize the parameters in Korea. However, these models are heavily reliant on the objective function and sample size during the optimization process. Moreover, the optimization process is carried out on the basis of single rainfall-runoff data, and the process is repeated for other events. Their averaged values over different parameter sets are usually used for practical purposes, leading to difficulties in the accurate simulation of discharge. In this sense, this paper proposed a hierarchical Bayesian model for estimating parameters of the Clark UH model. The proposed model clearly showed better performance in terms of Bayesian inference criterion (BIC). Furthermore, the result of this study reveals that the proposed model can also be applied to different hydrologic fields such as dam design and design flood estimation, including parameter estimation for the probable maximum flood (PMF).

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.453-470
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• 2013

In this paper, we consider a Bayesian analysis of the dose-effect relationship of cadmium to evaluate a benchmark dose(BMD). For this purpose, two dose-response curves commonly used in the toxicity study are fitted based on Bayesian methods to the data collected from the scientific literature on cadmium toxicity. Specifically, Bayesian meta-analysis and hierarchical modeling build an overall dose-effect relationship that use a piecewise linear model and Hill model, where the inter-study heterogeneity and inter-individual variability of dose and effect such as gender, age and ethnicity are accounted. Estimation of the unknown parameters is made by using a Markov chain Monte Carlo algorithm based user-friendly software WinBUGS. Benchmark dose estimates are evaluated for various cut-offs and compared with different tested subpopulations with with gender, age and ethnicity based on these two Bayesian hierarchical models.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.312-312
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• 2019

지구온난화로 인하여 기상학적 변동성 증가 및 수질, 수자원, 생태계 등의 다양한 영역에 영향을 야기하고 있으며, 이를 통한 피해가 전 세계적으로 증가하고 있는 추세이다. 이에 본 연구에서는 최근 다양한 분야에서 수문학적 빈도에 영향을 미친다고 알려진 AO(Arctic Oscillation), NAO(North Atlantic Oscillation), ENSO(El $Ni{\tilde{n}}o$-Southern Oscillation), PDO(Pacific Decadal Oscillation), MJO(Madden-Julian Oscillation)등의 외부인자중 SST, MJO를 활용하여 계절단위의 수문량 정도에서 기상학적 변량과 관측유역 강수량의 관계를 정립하고 발생 가능한 24시간 지속시간 극치강수량을 모의하였다. 이를 위하여 Bayesian 통계기법을 이용한 비정상성 빈도해석모형을 근간으로 외부 기상인자에 의한 계절강수량 예측모형인 계층적 베이지안 네트워크(Hierarchical Bayesian Network, HBN)를 구축한 후 산정된 결과를 입력 자료로 하여 직접적으로 일단위 이하의 극치강수량을 상세화 시킬 수 있는 베타 모델(four parameter beta, 4PB)을 연계한 계층적 베이지안 네트워크 베타모델(Hierarchical Bayesian Network-4beta Model, HBN4BM)을 개발하여 기상변동성을 고려한 상세화 모형을 개발하였다. 여름강수량 산정 결과 한강 유역의 경우 2016년은 관측값 573.85mm, 모의 값 567.15mm를 나타내어 약 1.2%의 오차를 나타냈으며, 2017년 및 2018년은 4.5%, 6.8%의 오차에서 모의가 이루어졌다. 금강의 경우 2016년은 다른 연도에 비하여 35.2%라는 큰 오차를 보였지만 불확실성 구간에서 모의가 이루어 졌으며, 2017년 및 2018년은 0.3%, 2.1%의 작은 오차가 발생하였다. 24시간 모의 결과는 최소 0.7%에서 최대 27.1%의 오차를 나타냈으며, 평균적으로 16.4%의 오차 결과가 모의되어 모형의 신뢰성을 확인하였다.