• 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.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.627-641
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• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.

• 한국환경복원기술학회지
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• 제3권1호
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• pp.1-9
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• 2000

The purpose of this study was to analyze the characteristics of soil erosion on the fill-slope of forest road by elapsed years after road construction. Thirteen plots were established on the fill-slope of the newly-constructed forest road, and surveyed for two years(1997~1998). In these plots, the data about soil erosion, surface runoff, vegetation coverage, slope structural characteristics and rainfall were collected. In 1997, the major causes for soil erosion were found by the correlation coefficients with the amount of surface runoff from the fill-slope, vegetation coverage, slope length, slope degree, total rainfall and max. 1 hour rainfall. But, in 1998, the major causes for soil erosion were vegetation coverage and slope degree. Using the stepwise multiple regression method, in 1997, the amount of soil erosion from the fill-slope was complexly expressed as a exponential function of statistically significant the amount of surface runoff from the fill-slope, total rainfall, slope degree of fill-slope and vegetation coverage, but, in 1998, simply expressed as a exponential function of vegetation coverage.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2003년도 가을 학술논문집
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• pp.590-595
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• 2003

An Exponential experiment system which is composed of a neutron detector, a signal analysis system and a neutron source, Cf-252 has been installed in order to experimentally determine the neutron effective multiplication factor for a PWR spent fuel assembly. The axial background neutron flux is measured in a preliminary performance test. From the results, the spacer grid position is determined to be consistent with the design specifications within a 2.3% relative error. The induced fission neutron for four of the assemblies is also measured by scanning the neutron source, Cf-252 or the neutron detector. The exponential decay constants have been evaluated by the application of the Poisson regression to the net induced fission neutron counts. The measured keffs determined on the basis of the exponential decay constants of Cl5 appeared to be 0.541, 0.540, 0.597 and 0.556, respectively, which are comparable with 0.55195$\pm$0.00232 of the MCNP calculation.

• International Journal of Precision Engineering and Manufacturing
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• 제9권1호
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• pp.12-18
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• 2008

This paper describes two run-to-run controllers, a nonlinear multiple exponential-weight moving-average (NMEWMA) controller and a dynamic model-tuning minimum-variance (DMTMV) controller, for photolithography processes. The relationships between the input recipes (exposure dose and focus) and output variables (critical dimensions) were formed using an experimental design method, and the photolithography process model was built using a multiple regression analysis. Both the NMEWMA and DMTMV controllers could update the process model and obtain the optimal recipes for the next run. Quantified improvements were obtained from simulations and real photolithography processes.

• Nutritional Sciences
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• 제9권3호
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• pp.201-211
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• 2006

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2011년도 춘계학술대회
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• pp.177-183
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• 2011

Korea has performed strategies for the third industrial accident prevention in order to minimize industrial accident. However, the occupational fatality rate and industrial accident rate appears to be stagnated for 11 years. Therefore, this paper forecasts the occupational fatality rate and industrial accident rate for 10 years. Also, this paper applies regression method (RA), exponential smoothing method (ESM), double exponential smoothing method (DESM), autoregressive integrated moving average (ARIMA) model and proposed analytical function method (PAFM) for trend of industrial accident. Finally, this paper suggests fundamental strategies for industrial accident prevention by forecasting of industrial accident rate in the long term.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.243-248
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• 2004

The timings of two successive events of interest may not be measurable, instead it may be right censored or interval censored; this data structure is called doubly censored data. In the study of HIV, two such events are the infection with HIV and the onset of AIDS. These data have been analyzed by authors under the assumption that infection time and induction time are independent. This paper investigates the regression problem when two events arc modeled to allow the presence of a possible relation between two events as well as a subject-specific effect. We derive the estimation procedure based on Goetghebeur and Ryan's (2000) piecewise exponential model and Gauss-Hermite integration is applied in the EM algorithm. Simulation studies are performed to investigate the small-sample properties and the method is applied to a set of doubly censored data from an AIDS cohort study.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.509-512
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• 2001

In this study, pollutant load from paddy field was estimated by regression equation from 5 to 8 in 2001. During study period, total rainfall was 511.3mm and runoff discharge was 968.71mm. Regression equation between flow rate(m3/s) and pollutant loading rate(g/s) is exponential relationship. For site 1, coefficient of determination (R2) for $COD_{cr}$, T-P, T-N were 0.7068, 0.8441, 0.6806 respectively and site 2, 0.9369, 0.8855, 0.4262 respectively. Considering unit loads, Jun was the highest valus as 13.85 $COD_{c}kg/km2/day$, 0.24 T-Pkg/km2/day, 1.22 T-Nkg/km2/day. Until study period, total $COD_{cr}$ load estimated regression equation is 19.32kg/km2/day and, T-P, T-N were 0.264, 1.88 respectively