• 응용통계연구
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• 제29권1호
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• pp.85-98
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• 2016

본 논문은 장외거래 수익률을 이용하여 추정한 여러 실현변동성들을 실증적으로 비교분석한다. 실제 금융 자산 시장에서는 장외시간이나 휴일에 거래가 적거나 드물게 나타나기 때문에 하루 전체의 실현변동성을 정확히 계산하는데 문제가 발생한다. 이를 해결하기 위해 제안되어진 장외거래 수익률을 여러 가지 방법으로 반영한 다양한 실현변동성의 추정치들에 대한 검토가 이루어진다. 실제 데이터의 실현변동성 추정치들의 예측정확성을 비교하기 위해 미국의 NASDAQ 지수와 S&P500 지수와 우리나라의 KOSPI 지수와 원/달러환율이 분석된다. 적분변동성의 불편추정치인 다음날의 로그수익률의 제곱을 기준으로 일일 실현 변동성의 추정치들은 비교되어지며 비교를 위해 절대평균오차(MAE)와 제곱평균오차근(RMSE)이 이용된다. 또한 통계적 추론을 위하여 Model Confidence Set(MCS) 방법과 Diebold-Mariano 검정법을 사용한다. 세 가지 주가지수 데이터에 대해 동일한 최적 방법이 선택되어지는데, 장외시간 수익률을 이용하여 장내시간 실현변동성의 크기 조정을 한 방법이다.

• 한국초지조사료학회지
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• 제36권3호
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• pp.223-236
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• 2016

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1349-1364
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• 2006

An analytic approach that provides explicit estimates of risk on cataract and epilation data is evaluated by reasonableness of conceivable relative risk models regarding a simple, odds, logistic or Gompertz regression method, assuming a binomial distribution. In these analyses, we apply relative risk models with two thresholds between epilators and nonepilators from a highly characteristic lesion of which radiation cataract does not occur around 2 gray for a single acute exposure. The risk models are fitted to the data assuming 10 as a constant relative biological effectiveness of neutron. The likelihood of observing the entire data set in these models fitted is evaluated by an individual binary-response array. Estimation of a threshold with or without severe epilation and the 100 ($1-\alpha$)% confidence limits are derived from the maximum likelihood approach. The relative risk model with two thresholds can be expressed as a formula with structure of Background $\times$ RR, where RR includes threshold models with or without epilation. The radiosensitivity of ionizing radiation to cataracts has been examined for the relationship between epilators and nonepilators.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.648-658
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• 2018

Regarding the fact that wind power forecast accuracy is gradually improved as time is approaching, this paper proposes a two-stage rolling dispatch approach based on model predictive control (MPC), which contains an intra-day rolling optimal scheme and a real-time rolling base point tracing scheme. The scheduled output of the intra-day rolling scheme is set as the reference output, and the real-time rolling scheme is based on MPC which includes the leading rolling optimization and lagging feedback correction strategy. On the basis of the latest measured thermal unit output feedback, the closed-loop optimization is formed to correct the power deviation timely, making the unit output smoother, thus reducing the costs of power adjustment and promoting wind power accommodation. We adopt chance constraint to describe forecasts uncertainty. Then for reflecting the increasing prediction precision as well as the power dispatcher's rising expected satisfaction degree with reliable system operation, we set the confidence level of reserve constraints at different timescales as the incremental vector. The expectation of up/down reserve shortage is proposed to assess the adequacy of the upward/downward reserve. The studies executed on the modified IEEE RTS system demonstrate the effectiveness of the proposed approach.

• The Journal of Asian Finance, Economics and Business
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• 제9권7호
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

• 대한교통학회지
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• 제14권1호
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• pp.43-50
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• 1996

This paper is concerned with assessing goodness of fit of gravity models. The Chi-square test, or one of its asymptotic equivalents, is usually recommended for the purpose. A difficulty that frequently arises, particularly when working with urban travel data, is that the expected number of trips for most origin-destination(O-D) pairs are small. In order to test goodness of fit of gravity model, a simple approach, which depends on the number of O-D pairs and certain trip totals being large, is proposed in this paper. In addition, derivation of variance of Chi-square ratio is proposed to test the confidence interval of Chi-square ratio and application of its results with simulated data set is made to verify the usefulness of the results.

• Korean Journal of Radiology
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• 제24권1호
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• pp.51-61
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• 2023

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent ¹⁸F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from ¹⁸F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

• ETRI Journal
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• 제46권2호
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• pp.277-289
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• 2024

In this paper, we describe a neural network-based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi-item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth-related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate-based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one-to-many mapping problem of multi-item recommendations. A fashion-domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi-item recommendation results, which is different from traditional accuracy evaluation.

• Archives of Plastic Surgery
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• 제50권3호
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• pp.254-263
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• 2023

Background The three-dimensional (3D) evaluation of skeletal stability after orthognathic surgery is a time-consuming and complex procedure. The complexity increases further when evaluating the surgery-first orthognathic approach (SFOA). Herein, we propose and validate a simple time-saving method of 3D analysis using a single software, demonstrating high accuracy and repeatability. Methods This retrospective cohort study included 12 patients with skeletal class 3 malocclusion who underwent bimaxillary surgery without any presurgical orthodontics. Computed tomography (CT)/cone-beam CT images of each patient were obtained at three different time points (preoperation [T0], immediately postoperation [T1], and 1 year after surgery [T2]) and reconstructed into 3D images. After automatic surface-based alignment of the three models based on the anterior cranial base, five easily located anatomical landmarks were defined to each model. A set of angular and linear measurements were automatically calculated and used to define the amount of movement (T1-T0) and the amount of relapse (T2-T1). To evaluate the reproducibility, two independent observers processed all the cases, One of them repeated the steps after 2 weeks to assess intraobserver variability. Intraclass correlation coefficients (ICCs) were calculated at a 95% confidence interval. Time required for evaluating each case was recorded. Results Both the intra- and interobserver variability showed high ICC values (more than 0.95) with low measurement variations (mean linear variations: 0.18 mm; mean angular variations: 0.25 degree). Time needed for the evaluation process ranged from 3 to 5 minutes. Conclusion This approach is time-saving, semiautomatic, and easy to learn and can be used to effectively evaluate stability after SFOA.

• 농촌계획
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• 제19권4호
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• pp.1-15
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• 2013

The purpose of this study is to quantitatively analyze the corresponding residents' benefit to get in the future from the district unit plan of the settlement zone by Cheongju local government. The target districts were selected as Gumdeong-gol district, Juseong district, and Shinmok-gol district, and Hyeonam district which had recently set their district unit plans. Specific targets to be analyzed were some infrastructure including roads, parking lots, parks and others. Using the analysis methods of the Logit model and non-parametric test approach, this study estimated to the average amounts to be willingly paid(WTP) by the residents in targeting settlement zones and tested the reliability of the average amounts of WTP. The results of analysis were as follows. First, it was found that the WTPs for infrastructure under 1st influence area in target districts were estimated as 5,233won for road, 2,826won for parking lot, and 3,705won for park respectively. For the WTPs for infrastructure under 1nd area were estimated as 4,910 won for road, 2,242won for parking lot, and 3,620won for park respectively. Second, as the result of calculating the non-parametric WTP which built the 95% confidence interval, it was found that the all WTPs except the WTP for parking lot were fell into the 95% confidence interval. Third, as the result of calculating yearly total sum of benefits that the residents would get from the 1st influence area and the 2nd influence area, it was found that Gumdeong-gol district were expected to get 62,376,472won-amounted benefits, and Juseong district, and Shinmok-gol district, and Hyeonam district would get 35,0778,016won, 26,886,560won, and 22,252,032won respectively.