• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.875-888
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• 2017

The Rayleigh distribution has been commonly used in life time testing studies of the probability of surviving until mission time. We focus on a reliability function of the Rayleigh distribution and deal with prior distribution on R(t). This paper is an effort to obtain Bayes estimators of rayleigh distribution with three different prior distribution on the reliability function; a noninformative prior, uniform prior and inverse gamma prior. We have found the Bayes estimator and predictive density function of a future observation y with each prior distribution. We compare the performance of the Bayes estimators under different sample size and in simulation study. We also derive the most plausible region, prediction intervals for a future observation.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.109-120
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• 1993

Using a noninformative prior and a gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p-th order statistic of n' future observations from the Burr distribution have been obtained. In additions, we examine the sensitivities of the results to the choice of model.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.407-416
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• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.171-176
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• 2003

This paper deals with the problem of predicting order statistics in samples from a Rayleigh population when an outlier is present. Bayesian predictive distribution and prediction bounds of the p-th order statistics is obtained where an outlier of type $\theta\delta$ is present. In this connection, some identies are derived.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.53-61
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• 1995

This paper deals with problems of predicting, based on the random censored sampling, a future observation and the p-th order statistic of n' future observations for the Rayleigh model. We consider the prediction intervals for the Rayleigh model with respect to an inverse gamma prior distribution. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.71-77
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• 1997

S independent sample 0,1,2, $\cdots$, s-1 (or stages 0,1,2, $\cdots$, s-1) are available from the Raleigh population. Procedure for predicting any order statistic in the $(s+1)^{th}$ sample is developed by obtaining the predictive distribution at stage s. Bounds for the sample size at stage S, in order to have the variance at stage S less than that at stage (s-1), are obtained.