• Journal of Technology Innovation
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• 제21권3호
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• pp.121-140
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• 2013

The establishment of function and role in government-funded research institute becomes a vital issue as fierce international technology-competition and enhanced industrial convergence makes science and technology important and influential more and in socio-economic level. This paper defines independent and dependent variables to identify factors which influence on research performance. As the independent variables, research funds, research personnel and research support staff are chosen. Dependent variables which are selected as proxy variables of research performance are royalties, papers and patents. Values from regression analysis were drawn by time-series analysis and cross-section analysis. As a result, the significance of correlation coefficients is sequential, research personnel, research support staff and research funds respectively. This finding is expected to give implication of future direction on government-funded research institute development.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1245-1245
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Nutrition Research and Practice
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• 제14권1호
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• pp.62-69
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• 2020

BACKGROUND/OBJECTIVES: A number of studies examined secular trends in blood lipid profiles using time series data of national surveys whereas few studies investigated individual-level factors contributing to such trends. The present study aimed to examine secular trends in dietary and modifiable factors and hyper-LDL-cholesterolemia (HC) prevalence and evaluate their associations using time series data of nationwide surveys. SUBJECTS/METHODS: The study included 41,073 Korean adults aged ≥ 30 years from the 2005, 2007-2009, 2010-2012, 2013-2015, and 2016 Korea National Health and Nutrition Examination Surveys. Stepwise logistic regression analysis was performed to select significant factors associated with HC, which was defined as serum LDL cholesterol levels ≥130 mg/dL. RESULTS: The following factors showed a positive association with HC (P < 0.05): for men having higher body mass index (BMI), being married, having an office job, and consuming higher dairy and vegetable oil products; for women having higher age or BMI, having no job or a non-office job, not in a low-income household, and consuming higher dairy products. In the given model, the 2016 survey data showed that a 2 kg/㎡ reduction in BMI of obese persons resulted in a decreased HC prevalence from 30.8% to 29.3% among men and from 33.6% to 32.5% among women. CONCLUSIONS: Based on these findings, it is suggested that primary prevention programs should advocate having proper BMI for Korean adults with a high-risk of HC. However, whether discouraging consumption of dairy and vegetable oil products can reduce HC prevalence warrants further studies with a prospective longitudinal design.

• Journal of Periodontal and Implant Science
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• 제48권1호
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• pp.3-11
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• 2018

Purpose: This study utilized a strong quasi-experimental design to test the hypothesis that the implementation of a policy to expand dental care services resulted in an increase in the usage of dental outpatient services. Methods: A total of 45,650,000 subjects with diagnoses of gingivitis or advanced periodontitis who received dental scaling were selected and examined, utilizing National Health Insurance claims data from July 2010 through November 2015. We performed a segmented regression analysis of the interrupted time-series to analyze the time-series trend in dental costs before and after the policy implementation, and assessed immediate changes in dental costs. Results: After the policy change was implemented, a statistically significant 18% increase occurred in the observed total dental cost per patient, after adjustment for age, sex, and residence area. In addition, the dental costs of outpatient gingivitis treatment increased immediately by almost 47%, compared with a 15% increase in treatment costs for advanced periodontitis outpatients. This policy effect appears to be sustainable. Conclusions: The introduction of the new policy positively impacted the immediate and long-term outpatient utilization of dental scaling treatment in South Korea. While the policy was intended to entice patients to prevent periodontal disease, thus benefiting the insurance system, our results showed that the policy also increased treatment accessibility for potential periodontal disease patients and may improve long-term periodontal health in the South Korean population.

• The Korean Journal of Applied Statistics
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• 제35권3호
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2017년도 학술발표회
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• pp.228-232
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• 2017

Extreme hydrological events can cause serious threats to the society. Hence, the selection of probability distributions for extreme rainfall is a fundamental issue. For this reason, this study was focused on understanding possible distributional changes in annual daily maximum rainfalls (AMRs) over time in Sri Lanka using quantile regression. A simplified nine-category distributional-change scheme based on comparing empirical probability density function of two years (i.e. the first year and the last year), was used to determine the distributional changes in AMRs. Daily rainfall series of 13 station over Sri Lanka were analyzed for the period of 1960-2015. 4 distributional change categories were identified for the AMRs. 5 stations showed an upward trend in all the quantiles (i.e. 9 quantiles: from 0.05 to 0.95 with an increment of 0.01 for the AMR) which could give high probability of extreme rainfall. On the other hand, 8 stations showed a downward trend in all the quantiles which could lead to high probability of the low rainfall. Further, we identified a considerable spatial diversity in distributional changes of AMRs over Sri Lanka.

• Journal of Korean Society of Transportation
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• 제5권2호
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• pp.55-72
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• 1987

The work analyzes the existing situation and defines special problems concerning traffic accidents in the two countries. The report is divided into three parts: 1) Using the global approach of SMEED, the data were evaluated using multiple regression analysis, and homogeneous groups of countries were defined by cluster analysis. In the global approach, the linear model is better than SMEED's non-linear model in explaining the number of fatalities. Among the different groups of countries, the linear approach was found to be better suited for industrialized countries and the non-linear approach better for the developing countries. T도 comparison of traffic fatality data for the Federal Republic the developing countries. The comparison of traffic fatality data for the Federal Republic of Germany and the Republic of Korea showed different regression equations during the same time period. 2) The BOX/JENKINS time series analysis on a monthly basis points out clearly similar seasonal patterns for the two countries over the years studied. The decrease in traffic accidents following the intensification of the safety belt requirement was proved in the ARIMA model. It amounts to 7 to 8 percent fewer personal injury accidents and fatal accidents. The identified increase in safety in the Federal Republic of Germany since the 1970s is mainly due to the reduction of accident severity in residential areas. 3) Speeds and headways on motorways in th3e two countries were also compared. The measurements point out that German road users drive faster, take more risks, and accept shorter time gaps than Korean road users. However, the accident statistics show accident rates for Korea that are several times higher than those in the Federal Republic of Germany.

• Journal of Intelligence and Information Systems
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• 제24권2호
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.601-611
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• 2021

There are many asset prices which are interlinked and have a bearing on the stock market index. Studies have shown that the interrelationship among these asset prices vary and are inconsistent. The ultimate aim of this study is to examine the dynamic relationship between gold price, oil price, exchange rate and stock index. Monthly time series data has been utilized by the researcher to examine the interrelationship between four variables. The relationship among stock exchange rate index, oil price and gold price have been undertaken using regression and granger causality test. The results indicate that the exchange rate and oil price have an indirect influence on NIFTY; whereas gold price had a direct impact on NIFTY. It is evident from the results that volatility in the price of gold is mainly dependent on the exchange rate and vice versa. All the variables affect NIFTY in some way or the other. However, gold has a direct and vital relationship. From the study findings, it can be concluded that macroeconomic variables like commodity prices and foreign exchange rate, gold and oil, have a strong relationship on the return on securities at the national stock exchange of India.