• 응용통계연구
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• 제24권5호
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• pp.951-961
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• 2011

본 논문에서는 제로팽창 음이항(ZINB) 회귀모형에서 회귀계수에 대한 추론방법으로 마코프체인몬테카를로(MC MC) 기법을 이용한 베이지안 추론방법을 제안하였다. 본 연구에서 고려한 ZINB 회귀모형은 반응변수의 평균뿐만 아니라 제로팽창확률에 대한 회귀모형을 고려한 것으로서 Jang, et al.(2010)의 연구를 확장한 것이다. 아울러 실제사례에 본 연구에서 제안한 베이지안 추론방법을 적용하고 과대산포를 허용하지 않는 제로팽창 포아송(ZIP) 회귀모형과 적합결과를 DIC를 이용하여 비교하였다. 실제 사례분석 결과 ZINB 회귀모형의 DIC가 ZIP모형보다 작게 나타나 ZINB 회귀모형이 ZIP 회귀모형보다 잘 적합되었음을 알 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제21권7호
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• pp.85-92
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• 2016

The law of large numbers, central limit theorem, and connection among binomial distribution, normal distribution, and statistical estimation require dynamics of continuous visualization for students' better understanding of the concepts. During this visualization process, the differences and similarities between statistical probability and mathematical probability that students should observe need to be provided with the intermediate steps in the converging process. We propose a visualization method that can integrate intermediate processes and results through Excel. In this process, students' experiences with dynamic visualization help them to perceive that the results are continuously changed and extracted from multiple situations. Considering modeling as a key process, we developed a classroom exercise using Excel to estimate the population mean and standard deviation by using a sample mean computed from a collection of data out of the population through sampling.

• 한국항행학회논문지
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• 제14권2호
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• pp.183-190
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• 2010

P2P 커뮤니티는 자발적으로 생성 및 소멸될 수 있는데 이는 Peer의 자유로운 참여로 조성되는 네트워크라는 구조적인 특징에 기반 한다. P2P에는 사용자들이 원하는 자원들을 공유할 수도 있지만, Fake 콘텐츠들과 같은 공유를 원하지 않는 자원들도 많다. 이러한 Fake 콘텐츠들을 제거하기 위한 하나의 방법으로 P2P 환경에서의 집단 지성(Collective Intelligence)을 이용하는 방법을 제안하며, 평판 시스템의 장점에 대해 시뮬레이션을 수행한다.

• 재무관리논총
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• 제3권2호
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• pp.329-364
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• 1996

Writing naked put options is a strategy employed either as a speculation to capture premium income, or as a method of placing a limit order to buy the underlying at the strike price in return for premium received. Using a Monte Carlo simulation, twenty thousand equity prices are generated under known volatility and return parameters. A binomial tree is constructed using the same volatility and return parameters. Put options on these 'equities' are valued with the binomial methodology. The performance of various put writing strategies is evaluated on a risk-adjusted basis. Evidence presented suggests that the judicious use of put options may enhance returns during portfolio construction.

• Journal of the Korean Statistical Society
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• 제20권1호
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• pp.57-66
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• 1991

This paper deals with the problem of estimating an arbitrary piecewise continuous function of the parameter under squared error loss in the one parameter exponential family. Using Blyth's(1951) method sufficient conditions are given for the admissibility of (possibly generalized Bayes) estimators. Also, some examples are provided for normal, binomial, and gamma distributions.

• Communications for Statistical Applications and Methods
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• 제17권1호
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• pp.127-140
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• 2010

본 논문에서는 중간분석에서 사용되는 집단축차 검정법으로 이산형 반응변수인 경우, 오류 분배율 함수를 적용한 집단축차 검정법을 제안한다. 특히 제 1종 오류와 제 2종 오류를 모두 적용한 집단축차 검정법을 제안하며, 기존의 오류 분배율 함수를 포함하는 새로운 오류 분배율 함수를 제안한다. 반응변수가 이산형인 경우 정확한 크기 ${\alpha}$ 검정을 할 수 없으므로 각 검정단계에 사용될 오류율을 분배하는 대신 각 검정단계까지 사용되어야 할 누적 오류율을 이용한다. 오류 분배율 함수를 적용한 집단축차 검정은 기존의 집단축차 검정 보다 빠른 연산과 유연한 검정이 가능하다는 장점을 지니고 있으며, 본 논문에서 제시된 오류 분배율 함수를 이용해 특성을 비교한다.

• Journal of Information Technology Applications and Management
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• 제28권1호
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• pp.33-41
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• 2021

In this study, we estimated the number of secondary COVID-19 infections caused by students with potential transmission potential home. When the existing Monte Carlo method was applied to Korean data, the average number of household members of the second COVID-19 infected was predicted. The summary of this study is as follows. First, in general, the number of secondary infections by students returning home from school is greatly influenced by the virus infection rate of each student group they contact while returning home from school. Korea-based empirical research on this is needed. Second, the number of secondary infections by Korean students was relatively lower than that of previous studies. This can be interpreted as being due to the domestic furniture structure. Third, unlike previous studies that assumed the distribution of secondary infected individuals as normal distribution, assuming a negative binomial distribution, the number of secondary infected individuals was sensitively changed according to the estimated parameters. Interpretation of this result shows that the number of secondary infections may vary depending on the time of decision making, the target region, and the target student group. Finally, according to the results of this analysis, a proposal was made to support education policy decisions.

• 청정기술
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• 제29권1호
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• pp.59-70
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• 2023

This study aims to introduce a biowaste processing system that uses spent coffee grounds and implement a real options method to evaluate the proposed process. Energy systems based on eco-friendly fuels lack sufficient data, and thus along with conventional approaches, they lack the techno-economic assessment required for great input qualities. On the other hand, real options analysis can estimate the different costs of options, such as continuing or abandoning a project, by considering uncertainties, which can lead to better decision-making. This study investigated the feasibility of a biowaste processing method using spent coffee grounds to produce biofuel and considered three different valuation models, which were the net present value using discounted cash flow, the Black-Scholes and binomial models. The suggested biowaste processing system consumes 200 kg/h of spent coffee grounds. The system utilizes a tilted-slide pyrolysis reactor integrated with a heat exchanger to warm the air, a combustor to generate a primary heat source, and a series of condensers to harness the biofuel. The result of the net present value is South Korean Won (KRW) -225 million, the result of the binomial model is KRW 172 million, and the result of the Black-Scholes model is KRW 1,301 million. These results reveal that a spent coffee ground-related biowaste processing system is worthy of investment from a real options valuation perspective.

• 산업경영시스템학회지
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• 제41권1호
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• 한국ITS학회 논문지
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• 제17권4호
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• pp.14-25
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• 2018

최근 빅데이터 시대의 도래와 함께 교통사고와 관련된 요인을 설명하기 용이해졌다. 이에따라 최신 분석 기법을 적용하여 교통사고 자료를 분석하고 시사점을 도출할 필요가 있다. 본 연구의 목적은 고속도로 교통사고 자료를 이용하여 고속도로의 주요 분석 단위인 콘존의 교통사고 건수를 예측하기 위하여 음이항 회귀모형과 딥 러닝을 이용한 기법을 적용하고 예측 성능을 비교하였다. 예측 성능 비교 결과, 딥 러닝 모형의 MOE들이 음이항 회귀모형에 비해 다소 우수한 것으로 나타났으나, MAD 기준으로 차이는 미미한 것으로 나타났다. 하지만 딥 러닝을 이용할 경우 다른 독립변수들을 추가하는 것이 용이하고, 모형의 구조 등을 변경할 경우 예측 신뢰도를 더욱 증가시킬 수 있을 것으로 판단된다.