• 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).

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.137.5-137
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• 2001

This paper proposes a method that combines diverse evidence relevant to the reliability to evaluate the reliability of complicated systems such as robots. In practice, reliability experts combine diverse evidences relevant to the reliability and infer the answers by using their own way that are mostly informal. The proposed method also combines diverse evidence and performs inferences but informal and quantitative way by using the benefits of Bayesian Belief Nets (BBN). Diverse evidences could be those from dassical analysis techniques, test results, quality assurance about the process of manufacturing, and the quality of the company or development team, etc. Some of these evidences are qualitative and others are quantitative. Both are ...

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.641-650
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• 2012

Spatial time series data can be viewed as a set of time series simultaneously collected at a number of spatial locations. This paper studies Bayesian inferences in a spatial time bilinear model with a Gibbs sampling algorithm to overcome problems in the numerical analysis techniques of a spatial time series model. For illustration, the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001~2009 are selected for analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.775-780
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• 2019

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.663-669
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• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1015-1025
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• 2017

Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2039-2044
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• 2006

Since the bio-information now even exceeds the capability of human brain, the techniques of data mining and artificial intelligent are needed to deal with the information in this field. There are many researches about using DNA microarray technique which can obtain information from thousands of genes at once, for developing new methods of analyzing and predicting of diseases. Discovering the mechanisms of unknown genes by using these new method is expecting to develop the new drugs and new curing methods. In this Paper, We tested accuracy on classification of microarray in Bayesian method to compare normalization method's Performance after dividing data in two class that is a feature abstraction method through a normalization process which reduce or remove noise generating in microarray experiment by various factors. And We represented that it improve classification performance in 95.89% after Lowess normalization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.393-405
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• 2020

Various geotechnical information is required to evaluate the stability of the ground and a foundation once liquefaction occurs due to earthquakes, such as the soil strength and groundwater level. The results of the Standard Penetration Test (SPT) conducted in Korea are registered in the National Geotechnical Information Portal System. If geotechnical information for a non-drilled area is needed, geostatistics can be applied. This paper is about the feasibility of obtaining ground information by the Empirical Bayesian Kriging (EBK) method and the Inverse Distance Weighting Method (IDWM). Esri's ArcGIS Pro program was used to estimate these techniques. The soil strength parameter of the drilling area and the level of groundwater obtained from the standard penetration test were cross-validated with the results of the analysis technique. In addition, Multichannel Analysis of Surface Waves (MASW) was conducted to verify the techniques used in the analysis. The Buk-gu area of Pohang was divided into 1.0 km×1.0 km and 110 zones. The cross-validation for the SPT N value and groundwater level through EBK and IDWM showed that both techniques were suitable. MASW presented an approximate section area, making it difficult to clearly grasp the distribution pattern and groundwater level of the SPT N value.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.179-193
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• 1991

In this paper, we introduce various methods of PR techniques initiated by S. Warner in 1960's and examine the maximum likelihood estimator for them. One of the main subjects of this paper is to represent Warner model, Unrelated Question Model, and Multi-Proportion Model in linear model. The other subject is to study the inference of PR model by using the Bayesian Approach.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.