• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.207-215
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• 2017

In this paper we study pooling effects in Bayesian testing procedures of independence for contingency tables from small areas. In small area estimation setup, we typically use a hierarchical Bayesian model for borrowing strength across small areas. This techniques of borrowing strength in small area estimation is used to construct a Bayes test of independence for contingency tables from small areas. In specific, we consider the methods of direct or indirect pooling in multinomial models through Dirichlet priors. We use the Bayes factor (or equivalently the ratio of the marginal likelihoods) to construct the Bayes test, and the marginal density is obtained by integrating the joint density function over all parameters. The Bayes test is computed by performing a Monte Carlo integration based on the method proposed by Nandram and Kim (2002).

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.609-622
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• 2011

The pattern of methylation draws significant attention from cancer researchers because it is believed that DNA methylation and gene expression have a causal relationship. As the interest in the role of methylation patterns in cancer studies (especially drug resistant cancers) increases, many studies have been done investigating the association between gene expression and methylation. However, a model-based approach is still in urgent need. We developed a finite mixture model in the Bayesian framework to find a possible relationship between gene expression and methylation. For inference, we employ Expectation-Maximization(EM) algorithm to deal with latent (unobserved) variable, producing estimates of parameters in the model. Then we validated our model through simulation study and then applied the method to real data: wild type and hydroxytamoxifen(OHT) resistant MCF7 breast cancer cell lines.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.597-606
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• 2005

The nonresponse in sample survey causes a problem when it comes time to analyze dataset in public-use files where the user has only complete-data methods available and has limited information about the reasons for nonresponse. Recently imputation for nonresponse is becoming a standard approach for handling nonresponse and various imputation methods have been devised . However, most imputation methods concern with continuous traits while many interesting features are measured by binary or ordered categorical scales in sample survey. In this note. an imputation method for ignorable nonresponse in binary or ordered categorical traits is considered.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.336-342
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• 2001

One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts decision processes.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1423-1431
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• 2016

When the sample size in a certain domain is too small to produce adequate information, small area model with random effects is usually used. Also, if we do not consider an inherent pattern which data possess, it considerably affects inference. In this paper, we mainly focus on modeling to handle increased variation of the Current Population Survey (CPS) median income as the Internal Revenue Service (IRS) mean income increases. In a hierarchical Bayesian framework, most estimations are carried out through the Gibbs sampler while the grid method is used to generate parameters from non-standard form. Numerical study indicates that the performance of proposed model is better than that of CPS method in terms of four comparison measurements.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.181-181
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• 2021

유역 내의 물순환 평가를 위하여 적합한 강우-유출모형을 선정하고 적용하는 것은 수문학적 관점에서 주된 과제이다. 장기적인 관점의 수자원 관리를 위해서는 직접적인 계측을 통해 장기간의 유출자료를 취득하는 방법이 있으나, 국내의 주요지점을 제외한 대다수의 중소규모의 지점에 계측기를 설치하는 것은 현실적으로 어려우므로, 자료취득이 비교적 용이하고 신뢰성이 높은 장기간 강우 자료를 강우-유출모형의 입력자료로 활용하여 미계측 유역으로의 모형을 확장하는 방안이 적절하다는 평가를 받고 있다. 본 연구는 국내외 주요 연속강우-유출모형의 특성을 파악하기 위하여 비교적 신뢰성 있는 자료를 보유하고 있는 소양강댐 유역에 다수의 연속강우-유출모형을 적용하였다. 모델링 결과로 산출된 유황곡선(flow duration curve)을 소양강댐 유입량과 비교하여 각 모형의 특징을 파악하고 유량에 따른 적합성 평가를 진행하였다. 또한, 향후 미계측유역으로 모형을 확장하기 위하여 매개변수 개수 및 재현능력을 동시에 평가하였다. 다수의 모형 중 적합성이 높은 모형들을 선별하였으며, 선별된 모형들의 불확실성을 고려함과 동시에 계층적 베이지안 기법을 활용하여 최종적으로 앙상블모형을 제시하였다. 앙상블모형을 단일 모형과 비교한 결과 단일 모형보다 개선된 성능을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.354-362
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• 2010

This paper proposes a new method for a hierarchical analysis of human gait by dividing the motion into gait direction and gait posture using the tool of dynamic Bayesian network. Based on Factorial HMM (FHMM), which is a type of DBN, we design the Gait Motion Decoder (GMD) in a circular architecture of state space, which fits nicely to human walking behavior. Most previous studies focused on human identification and were limited in certain viewing angles and forwent modeling of the walking action. But this work makes an explicit and separate modeling of pedestrian pose and posture to recognize gait direction and detect orientation change. Experimental results showed 96.5% in pose identification. The work is among the first efforts to analyze gait motions into gait pose and gait posture, and it could be applied to a broad class of human activities in a number of situations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.