• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.584-591
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• 2010

In this paper, we propose an adaptive Relay Station (RS) coverage extension scheme for the Mobile Multi-hop Relay (MMR) systems. The MMR system with a non-transparent mode RS can be used to extend BS coverage using the remaining capacity of the Base Station(BS). Thus, the call blocking may occur in RSs when calls arrive very often in Multi-hop Relay Base Station (MR-BS). In the proposed scheme, RSs can be connected to the neighbor MMR system as the 2ndtier RSs if the neighbor MMR system services low traffic load when calls are frequently arrived in MR-BS or RSs. By doing so, the MMR system can accept a new call without call blocking. Through numerical results, we demonstrate that the proposed scheme outperforms the conventional MMR system in terms of the throughput and call blocking probability of MMR systems.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.781-790
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• 2015

The Parrondo paradox is the counter-intuitive phenomenon: when we combine two losing games we can win the game or when we combine two winning games we can lose the game. In this paper, we assume that an investor adopts the rule of the history-dependent Parrondo game for investment in the stock market. Using the KRX (Korea Exchange) data from 2012 to 2014, we found the Parrondo paradox in the stock trading: the redistribution of profits among accounts can turn the decrease of the expected cumulative profit into the increase of the expected cumulative profit. We also found that the opposite case, namely the reverse Parrondo effect, can happen in the stock trading.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.53-58
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• 2006

MicroRNA (miRNA)는 약 22 nt의 작은 RNA 조각으로 이루어져 있으며 stem-loop 구조의 precursor 형태에서 최종적으로 만들어 진다. miRNA는 mRNA의 3‘UTR에 상보적으로 결합하여 유전자의 발현을 억제하거나 mRNA의 분해를 촉진한다. miRNA를 동정하기 위한 실험적인 방법은 조직 특이적인 발현, 적은 발현양 때문에 방법상 한계를 가지고 있다. 이러한 한계는 컴퓨터를 이용한 방법으로 어느 정도 해결될 수 있다. 하지만 miRNA의 서열상의 낮은 보존성은 homology를 기반으로 한 예측을 어렵게 한다. 또한 기계학습 방법인 support vector machine (SVM) 이나 naive bayes가 적용되었지만, 생물학적인 의미를 해석할 수 있는 generative model을 제시해 주지 못했다. 본 연구에서는 우수한 miRNA 예측을 보일 뿐만 아니라 학습된 모델로부터 생물학적인 지식을 얻을 수 있는 Bayesian network을 적용한다. 이를 위해서는 생물학적으로 의미 있는 특질들의 선택이 중요하다. 여기서는 position weighted matrix (PWM)과 Markov chain probability (MCP), Loop 크기, Bulge 수, spectrum, free energy profile 등을 특질로서 선택한 후 Information gain의 특질 선택법을 통해 예측에 기여도가 높은 특질 25개 와 27개를 최종적으로 선택하였다. 이로부터 Bayesian network을 학습한 후 miRNA의 예측 성능을 10 fold cross-validation으로 확인하였다. 그 결과 pre-/mature miRNA 각 각에 대한 예측 accuracy가 99.99% 100.00%를 보여, SVM이나 naive bayes 방법보다 높은 결과를 보였으며, 학습된 Bayesian network으로부터 이전 연구 결과와 일치하는 pre-miRNA 상의 의존관계를 분석할 수 있었다.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

• Journal of Preventive Medicine and Public Health
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• v.24 no.4 s.36
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• pp.485-495
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• 1991

This study examined the effects of referral requirements for insurance patients which have been enforced since July 1, 1989 when medical insurance coverage was extended to the whole population except beneficiaries of medical assistance program. The requirements are mainly aimed at discouraging the use of tertiary care hospitals by imposing restrictions on the patient's choice of a medical service facility. The expectation is that such change in the pattern of medical care utilization would produce several desirable effects including increased efficiency in patient care and balanced development of various types of medical service facilities. In this study, these effects were assessed by the change in the number of out-patient visits and bed-days per illness episode and the share of each type of facility in the volume of services and the amount of expenditures after the implementation of the new referral system. The data for analysis were obtained from the claims to the insurance for government and school employees. The sample was drawn from the claims for the patients treated during the first six months of 1989, prior to the enforcement of referral requirements, and those of the patients treated during the first six months of 1990, after the enforcement. The 1989 sample included 299,824 claims (3.6% of total) and the 1990 sample included 332,131 (3.7% of total). The data were processed to make the unit of analysis an illness episode instead of an insurance claim. The facilities and types of care utilized for a given illness episode are defined to make up the pathway of medical care utilization. This pathway was conceived of as a Markov Chain process for further analysis. The conclusion emerged from the analysis is that the enforcement of referral requirements resulted in less use of tertiary care hospitals, and thereby decreased the volume of services and the amount of insurance expenses per illness episode. However, there are a few points that have to be taken into account in relation to the conclusion. The new referral system is likely to increase the use of medical services not covered by insurance, so that its impact on national health expenditures would be different from that on insurance expenditures. The extension of insurance coverage must have inereased patient load for all types of medical service organizations, and this increase may be partly responsible for producing the effects attributed to the new referral system. For example, excessive patient load for tertiary care hospitals may lead to the transfer of their patients to other types of facilities. Another point is that the data for this study correspond to very early phase of the new system. But both patients and medical care providers would adapt themselves to the new system to avoid or overcome its disadvantages for them, so as that its effects could change over time. Therefore, it is still necessary to closely monitor the impact of the referral requirements.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.132-144
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• 2023

Recently, the frequency and intensity of heat waves due to the increase in climate change temperature are increasing. Therefore, this study tried to compare the evaluation process and evaluation results of the heat wave disaster evaluation, which is the government's analysis of the heat wave disaster vulnerability and the risk evaluation method recently emphasized by the IPCC. The analysis of climate change disaster vulnerability is evaluated based on manuals and guidelines prepared by the government. Risk evaluation can be evaluated as the product of the possibility of a disaster and its impact, and it is evaluated using the Markov chain Monte Carlo simulation based on Bayesian estimation method, which uses prior information to infer posterior probability. As a result of the analysis, the two evaluation results for Busan Metropolitan City differed slightly in the spatial distribution of areas vulnerable to heat waves. In order to properly evaluate disaster vulnerable areas due to climate change, the process and results of climate change disaster vulnerability analysis and risk assessment must be reviewed, and consider each methodology and countermeasures must be prepared.

• Asia Marketing Journal
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• v.15 no.3
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• pp.53-77
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• 2013

As advertising and promotions are categorized as operating expenses, managers tend to reduce marketing budget to improve their short term profitability. Gauging the value and accountability of marketing spending is therefore considered as a major research priority in marketing. To respond this call, recent studies have documented that financial market reacts positively to a firm's marketing activity or marketing related outcomes such as brand equity and customer satisfaction. However, prior studies focus on the relation of marketing variable and financial market variables. This study suggests a channel about how marketing activity increases firm valuation. Specifically, we propose that a firm's marketing activity increases the level of the firm's product market information and thereby the dispersion in financial analysts' earnings forecasts decreases. With less uncertainty about the firm's future prospect, the firm's managers and shareholders have less information asymmetry, which reduces the firm's cost of capital and thereby increases the valuation of the firm. To our knowledge, this is the first paper to examine how informational benefits can mediate the effect of marketing activity on firm value. To test whether marketing activity contributes to increase in firm value by mitigating information asymmetry, this study employs a longitudinal data which contains 12,824 firm-year observations with 2,337 distinct firms from 1981 to 2006. Firm value is measured by Tobin's Q and one-year-ahead buy-and-hold abnormal return (BHAR). Following prior literature, dispersion in analysts' earnings forecasts is used as a proxy for the information gap between management and shareholders. For model specification, to identify mediating effect, the three-step regression approach is adopted. All models are estimated using Markov chain Monte Carlo (MCMC) methods to test the statistical significance of the mediating effect. The analysis shows that marketing intensity has a significant negative relationship with dispersion in analysts' earnings forecasts. After including the mediator variable about analyst dispersion, the effect of marketing intensity on firm value drops from 1.199 (p < .01) to 1.130 (p < .01) in Tobin's Q model and the same effect drops from .192 (p < .01) to .188 (p < .01) in BHAR model. The results suggest that analysts' forecast dispersion partially accounts for the positive effect of marketing on firm valuation. Additionally, the same analysis was conducted with an alternative dependent variable (forecast accuracy) and a marketing metric (advertising intensity). The analysis supports the robustness of the main results. In sum, the results provide empirical evidence that marketing activity can increase shareholder value by mitigating problem of information asymmetry in the capital market. The findings have important implications for managers. First, managers should be cognizant of the role of marketing activity in providing information to the financial market as well as to the consumer market. Thus, managers should take into account investors' reaction when they design marketing communication messages for reducing the cost of capital. Second, this study shows a channel on how marketing creates shareholder value and highlights the accountability of marketing. In addition to the direct impact of marketing on firm value, an indirect channel by reducing information asymmetry should be considered. Potentially, marketing managers can justify their spending from the perspective of increasing long-term shareholder value.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1919-1927
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• 1993

A new MAC protocol has been proposed and analysed to relieve the unfairness problems exhibited by the basic version of the DQDB standard. DQDB MAC protocol has the unfairness problems in throughputs. message delay and so or. And when the slots are reused or the file transmissions takes long, the unfairness problems in the system become worse. The new access protocol proposed here, which of called the Balanced DQDB, guarantees a fair bandwidth distribution by using one bit of the dual bus network protocol and keeps up all characteristics of DQDB. the DQDB analysis model introduced by Wen Jing, et al, was considered to analyse a sequential balance distribution of solts. And the probabilities of the empty in operation mode were represented to determine the probabilities for busy bits to generate on each node of the bus using the Markov chain. Through the simulations. the performances of the proposed Balanced DQDB and that of the standard DQDB of the BWB mechanism were compared at the state that the values of the RQ or CD counter on each node varied dynamically. As the results, it is shown that the Balanced DQDB has the decrement of throughputs in upstream, but the numbers of the used empty slots at each node of the Balanced DQDB had more than that of the others because the Balanced DQDB has over 0.9 throughputs in the 70~80% nodes of total node and it has constant throughputs at each node. And there results were analogous to that of the analytical model.