• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.150-150
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• 2017

기후변화로 인하여 극한 홍수와 극한 가뭄 발생이 증가할 것으로 전망하고 있어 이에 대한 위험이 대두되고 있는 실정이다. 홍수 및 가뭄 수문시계열의 빈도해석시에 일반적으로 활용되는 정상성 빈도해석기법은 수문자료의 정상성을 기반으로 한 빈도해석이 대부분이기 때문에 기후변화 및 수문자료의 비정상성을 반영한 새로운 빈도해석 기법이 요구되고 있는 상황이다. 본 연구에서는 5개의 대표 관측지점(서울, 포항, 추풍령, 여수, 광주)를 선별하고 1976년부터 2015년까지 일강우자료를 활용하여 기상학적 가뭄지수인 SPI(Standardized Precipitation Index)를 산정하였다. 산정한 SPI의 경향성을 Mann-Kendall 분석을 하였으며, 정상성 및 비정상성 빈도해석을 위하여 최적확률분포로 선정된 GEV 분포 적용하였다. 본 연구에서는 가뭄빈도해석을 위하여 SPI를 입력자료로 활용하였으며, 산정된 SPI의 비정상성을 반영한 비정상성 빈도해석의 경우 Bayesian 모형을 기반으로 한 MCMC(Markov Chain Monte Carlo) 모의를 이용하여 극치분포의 사후분포 매개변수를 추정하였다. 추정 값을 바탕으로 하여 가뭄의 관측소별 빈도해석을 실시하였고 재현기간별-지속기간별 가뭄심도를 추정하여 관측소별 가뭄심도-지속기간-빈도(SDF,Severity-Duration-Frequency) 곡선을 유도하였다. 본 연구를 통하여 정상성과 비정상성 빈도해석 결과의 비교연구를 수행하였으며 기후변화에 따른 비정상 시계열로 구성된 가뭄빈도해석에 매우 유용하게 적용될 수 있을 것으로 나타났다.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.665-672
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• 2021

The paper examines the impact of shadow economy and corruption, along with public expenditure, trade openness, foreign direct investment (FDI), inflation, and tax revenue on the economic growth of the BRICS countries. Data were collected from the World Bank, Transparency International, and Heritage Foundation over the 1991-2017 period. The Bayesian linear regression method is used to examine whether shadow economy, corruption and other indicators affect the economic growth of countries studied. This paper applies the normal prior suggested by Lemoine (2019) while the posterior distribution is simulated using Monte Carlo Markov Chain (MCMC) technique through the Gibbs sampling algorithm. The results indicate that public expenditure and trade openness can enhance the BRICS countries' economic growth, with the positive impact probability of 75.69% and 67.11%, respectively. Also, FDI, inflation, and tax revenue positively affect this growth, though the probability of positive effect is ambiguous, ranging from 51.13% to 56.36%. Further, the research's major finding is that shadow economy and control of corruption have a positive effect on the economic growth of the BRICS countries. Nevertheless, the posterior probabilities of these two factors are 62.23% and 65.25%, respectively. This result suggests that their positive effect probability is not high.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.101-108
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• 2011

The turnover rate of information security professionals in Korea is over 10% and turnover into non-information security fields accounts for over 50% of all the turnovers [1]. It is not only important to recruit a new quality workforce, but also to make the current workforce perform satisfactorily, to improve their performance, and eventually to attain information security objectives. This study proposes a Markov chain model for the turnover process of information security professionals and forecasts the job duty composition of information security professionals. The results of this study can be applied to secure the justification of government policies for the promotion of information security professionals.

• Fisheries and Aquatic Sciences
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• v.24 no.4
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• pp.139-152
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• 2021

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.363-370
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• 2022

The study aims to probe the impact of foreign ownership on Vietnamese listed firms' capital structure. This study employs panel data of 288 non-financial firms listed on the Ho Chi Minh City stock exchange (HOSE) and Ha Noi stock exchange (HNX) in 2015-2019. In this research, we applied a Bayesian linear regression method to provide probabilistic explanations of the model uncertainty and effect of foreign ownership on the capital structure of non-financial listed enterprises in Vietnam. The findings of experimental analysis by Bayesian linear regression method through Markov chain Monte Carlo (MCMC) technique combined with Gibbs sampler suggest that foreign ownership has substantial adverse effects on the firms' capital structure. Our findings also indicate that a firm's size, age, and growth opportunities all have a strong positive and significant effect on its debt ratio. We found that the firms' profitability, tangible assets, and liquidity negatively and strongly affect firms' capital structure. Meanwhile, there is a low negative impact of dividends and inflation on the debt ratio. This research has ramifications for business managers since it improves a company's financial resources by developing a strong capital structure and considering foreign investment as a source of funding.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.51-56
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• 2022

In space based constellation network, users are allowed to enter or leave the network arbitrarily. Hence, the number, identities and transmitted data of active users vary with time and have considerable impacts on the receiver's performance. The so-called problem of multiuser detection means identifying the identity of each active user and detecting the data transmitted by each active user. Traditional methods assume that the number of active users is equal to the maximum number of users that the network can hold. The model of traditional methods are simple and the performance are suboptimal. In this paper a Maximum A Posteriori Probability (MAP) based multiuser detection method is proposed. The proposed method models the activity state of users as Markov chain and transforms multiuser detection into searching optimal path in grid map with BCJR algorithm. Simulation results indicate that the proposed method obtains 2.6dB and 1dB E_b/N₀ gains respectively when activity detection error rate and symbol error rate reach 10^-3, comparing with reference methods.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.1-8
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• 2009

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. The optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement is generally accepted. The Bayesian parametric inference of model using log Poisson execution time employ tool of Markov chain(Gibbs sampling and Metropolis algorithm). In a numerical example by T1 data was illustrated. make out estimating software optimal release time from the maximum likelihood estimation and Bayesian parametric estimation.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.32.3-33
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• 2021

We present H I gas kinematics and star formation activities of NGC 6822, a dwarf galaxy located in the Local Volume at a distance of ~ 490 kpc. We perform profile decomposition of the line-of-sight velocity profiles of the high-resolution (42.4" × 12" spatial; 1.6 km/s spectral) H I data cube taken with the Australia Telescope Compact Array (ATCA). For this, we use a new tool, the so-called BAYGAUD (BAYesian GAUssian Decompositor) which is based on Bayesian Markov Chain Monte Carlo (MCMC) techniques, allowing us to decompose a line-of-sight velocity profile into an optimal number of Gaussian components in a quantitative manner. We classify the decomposed H I gas components of NGC 6822 into bulk-narrow, bulk-broad, and non_bulk with respect to their velocity and velocity dispersion. We correlate their gas surface densities with the surface star formation rates derived using both GALEX far-ultraviolet and WISE 22 micron data to examine the impact of gas turbulence caused by stellar feedback on the Kennicutt-Schmidt (K-S) law. The bulk-narrow component that resides within r25 is likely to follow the linear extension of the Kennicutt-Schmidt (K-S) law for molecular hydrogen (H2) at the low gas surface density regime where H I is not saturated.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.139-139
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• 2021

Flooding events often result from extreme precipitations driven by various climate mechanisms, which are often disregarded in flood risk assessments. To bridge this gap, we propose a climate-mechanism-based flood frequency analysis that accommodates the direct linkage between the dominant climate processes and risk management decisions. Several statistical methods have been utilized in this approach including the Markov Chain analysis, K-nearest neighbor (KNN) resampling approach, and Z-score-based jittering method. After that, the impacts of climate change are associated with the modification of the transition matrix (TM) and the application of the quantile mapping approach. For this study, we have selected the Nam River Basin, South Korea, to consider the heterogeneous impacts of the two climate mechanisms, including the Tropical Cyclone (TC) and non-TCs. Based on our results, while both climate mechanisms have significant impacts on future flood extremes, TCs have been observed to bring more significant and immediate impacts on the flood extremes. The results in this study have proven that the proposed approach can lead to a new insights into future flooding management.