• The Korean Journal of Financial Management
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• v.16 no.2
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• pp.215-241
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• 1999

본 연구는 우리나라 상장기업중 금융 보험업을 제외하고 비교적 상장기업수가 많은 9개 산업에서 임의로 선정한 180개 표본기업을 분석대상으로 하였다. 1989년 1월부터 1996년 12월까지를 분석대상기간으로 설정하여 베타계수 예측능력을 향상시키기 위한 회계위험변수모형의 예측능력을 평가하고 위험수준별 예측능력에 차이가 있는지도 분석하였다. 아울러 베타계수 추정시 사용된 수익률 측정간격에 빠른 베타계수의 안정성과 회계위험변수모형의 예측능력을 분식하였다. 본 연구의 중요한 결과를 요약하면 다음과 같다. 첫째, 포트폴리오를 구성한 경우 수익률 측정기간에 관계없이 일관되게 예측오차가 유의적으로 적게 나타나 회계위험변수모형의 베타계수 예측능력이 우수하였으며 베타계수예측에 회계 변수의 유용성이 확인되었다. 둘째, 위험수준에 따른 베타계수의 안정성 분석에서는 중위험집단의 베타가 안정성이 높았으며 고위험집단에서 예측오차가 가장 크게 나타나 불안정하였다. 회계위험변수모형의 예측능력은 위험수준에 관계없이 단순모형보다 우수하여 베타예측에 회계정보의 유용성을 일반화시킬 수 있을 것이다. 셋째, 수익률 측정간격에 따른 베타계수의 안정성과 예측능력 분석에서는 월별수익률을 이용하는 경우보다 주별수익률을 이용하는 경우 추정베타의 안정성이 높고 베타계수 예측모형의 예측능력이 향상되는 것으로 나타났다. 넷째, OLS베타를 수정하지 않고 이용하는 경우보다 Bayesian 기법으로 수정한 Bayesian수정 베타를 이용할 경우 예측오차가 감소하여 Bayesian 수정기법의 유용성이 확인되었다.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.165-168
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• 2006

Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.384-389
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• 2000

A artificial neural network model was developed to analyze and forecast the flow fluctuation at small streams in the Balan watershed. Backpropagation neural networks were found to perform very well in forecasting daily streamflows. In order to deal with slow convergence and an appropriate structure, two algorithms were proposed for speeding up the convergence of the backpropagation method, and the Bayesian Information Criterion(BIC) was proposed for obtaining the optimal number of hidden nodes. From simulations using daily flows at the HS#3 watershed of the Balan Watershed Project, which is 412,5 ㏊ in size and relatively steep in landscape, it was found that those algorithms perform satisfactorily.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.72-75
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• 2006

Event trees are widely used graphical tool to denote the accident inintiation and escalation to more severe accident. But they have some drawbacks in that they do not have efficient way of updating model parameters and also they can not contain the information about dependency or independency among model parameters. A tool that can cure such drawbacks is an influence diagram. We introduce influence diagrams and explain how to update model parameters and obtain predictive distributions. We show that an event tree can be converted to a statistically equivalent influence diagram, and bayesian prediction can be made more effectively through the use of influence diagrams.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4501-4504
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• 2012

In recent decades, decreasing trends in esophageal cancer mortality have been observed across China. We here describe esophageal cancer mortality trends in Linzhou city, a high-incidence region of esophageal cancer in China, during 1988-2010 and make a esophageal cancer mortality projection in the period 2011-2020 using a Bayesian approach. Age standardized mortality rates were estimated by direct standardization to the World population structure in 1985. A Bayesian age-period-cohort (BAPC) analysis was carried out in order to investigate the effect of the age, period and birth cohort on esophageal cancer mortality in Linzhou during 1988-2010 and to estimate future trends for the period 2011-2020. Age-adjusted rates for men and women decreased from 1988 to 2005 and changed little thereafter. Risk increased from 30 years of age until the very elderly. Period effects showed little variation in risk throughout 1988-2010. In contrast, a cohort effect showed risk decreased greatly in later cohorts. Forecasting, based on BAPC modeling, resulted in a increasing burden of mortality and a decreasing age standardized mortality rate of esophageal cancer in Linzhou city. The decrease of esophageal cancer mortality risk since the 1930 cohort could be attributable to the improvements of socialeconomic environment and lifestyle. The standardized mortality rates of esophageal cancer should decrease continually. The effect of aging on the population could explain the increase in esophageal mortality projected for 2020.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1003-1016
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• 2014

Proper missing data imputation is an important procedure to obtain superior results for data analysis based on survey data. This paper deals with both a model based imputation method and model estimation method. We utilized a Bayesian method to solve a boundary solution problem in which we applied a maximum likelihood estimation method. We also deal with a missing mechanism model selection problem using forecasting results and a comparison between model accuracies. We utilized MWPE(modified within precinct error) (Bautista et al., 2007) to measure prediction correctness. We applied proposed ML and Bayesian methods to the Korean presidential election exit poll data of 2012. Based on the analysis, the results under the missing at random mechanism showed superior prediction results than under the missing not at random mechanism.

• Geomechanics and Engineering
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• v.22 no.4
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• pp.291-303
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• 2020

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.