• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.891-901
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• 2009

In this paper, we investigate the approach to estimate VaR under the transformed GARCH model. The time series are transformed to approximate to the underlying distribution of error terms and then the parameters and the one-sided prediction interval are estimated with the transformed data. The back-transformation is applied to compute the VaR in the original data scale. The analyses on the asset returns of KOSPI and KOSDAQ are presented to verify the accuracy of the coverage probabilities of the proposed VaR.

• The Magazine of the IEIE
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• v.30 no.9
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• pp.1002-1011
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• 2003

In this paper, we introduce an integrated SoC front-end design & verification environment which can be practically used in the embedded 32-bit processor-core SoC VLSI design. Our introduced SoC design & verification environment integrates two most important flows, such as the RTL power estimation and code coverage analysis, with the functional verification (chip validation) flow which is used in the conventional simulation-based design. For this, we developed two simulation-based inhouse tools, RTL power estimator and code coverage analyzer, and used them to adopt them to our RTL design and to increase the design quality of that. Our integrated design environment also includes basic design and verification flows such as the gate-level functional verification with back annotation information and test vector capture & replay environment.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.59-74
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• 2004

The paper considers the Bayesian interval estimation for the common mean of several normal populations. A Bayesian procedure is proposed based on the idea of matching asymptotically the coverage probabilities of Bayesian credible intervals with their frequentist counterparts. Several frequentist procedures based on pivots and P-values are introduced and compared with Bayesian procedure through simulation study. Both simulation results demonstrate that the Bayesian procedure performs as well or better than any available frequentist procedure even from a frequentist perspective.

• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.69-81
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• 2012

Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.171-181
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• 2006

The Wald confidence interval has been considered as a standard method for the difference of proportions. However, the erratic behavior of the coverage probability of the Wald confidence interval is recognized in various literatures. Various alternatives have been proposed. Among them, Agresti-Caffo confidence interval has gained the reputation because of its simplicity and fairly good performance in terms of coverage probability. It is known however, that the Agresti-Caffo confidence interval is conservative. In this note, a confidence interval is developed using the weighted Polya posterior which was employed to obtain a confidence interval for the binomial proportion in Lee(2005). The resulting confidence interval is simple and effective in various respects such as the closeness of the average coverage probability to the nominal confidence level, the average expected length and the mean absolute error of the coverage probability. Practically it can be used for the interval estimation of the difference of proportions for any sample sizes and parameter values.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.21-28
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• 2011

Site conditions affect the magnitude of loss due to geologic hazards including, but not limited to, earthquakes, landslides and liquefaction. Reliable geologic loss estimation system requires site information which can be achieved by GIS-based method using geologic or topographic maps. Slope data derived from DEM can be an effective indicator for classifying the site conditions. We studied and discussed the effect of different DEM resolutions in the site classification. We limited the study area to the south-eastern Korea and used two different resolutions of DEMs to observe discrepancies in the site classification results. Largest discrepancy is observed in the areal coverage of site class C(very dense soil and soft rock) and E(soft soil). Comparison of results shows that more areas are classified as site class B(general rock) or E(soft soil) when we use higher resolution DEM. The comparison also shows that more areas are classified as site class C or D(stiff soil) using lower resolution DEM. The comparison of results using resampled DEMs with different resolutions shows that the areal coverage of site class B and E decreases with decreasing resolutions. On the contrary, areal coverage of site class C and D increase with decreasing resolutions. Loss estimation system can take advantage of higher-resolution DEMs in the area of rugged or populated to obtain precise local site information.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.1
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• pp.18-27
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• 2009

The purpose of this study is to understand the scale of temperature change following large-scale urban developments in paddy fields to present possible measures to preserve suburban area paddy fields and to lower the scale of temperature increase after developing paddy fields in urban areas. The study was conducted in Bupyeong and Bucheon of Incheon Metropolitan City. The satellite image($1989{\sim}2000$) before and after the development of old paddy fields were used to analyze the land surface temperature changes according to the land use types. Building coverage, green coverage, non-permeable pavement coverage, and floor area ratio(FAR) were selected as the factors that influence urban temperature changes and the temperature estimation model was constructed by using correlation and regression analyses. The before and after satellite images of Bupyeong and Bucheon were classified into forests, greens and plantations, paddy fields, unused lands, and urban areas. The results indicate that most of the paddy fields that existed in the center of Bupyeong and Bucheon were converted into unused lands which were undergoing construction to become new urban areas. The difference between the surface temperatures of May 17th, 1989 and May 7th, 2000 was analyzed to reveal that most land converted from paddy fields to unused lands or urban areas saw an increase in surface temperature. Han River was used as a comparison to analyze the average surface temperature changes($1989{\sim}2000$) in former paddy fields. The scale of temperature changes were: $+1.6697^{\circ}C$ in urban parks; $+2.5503^{\circ}C$ in residential zones; $+2.9479^{\circ}C$ on public lands, $+3.0385^{\circ}C$ in commercial zones, and $+3.1803^{\circ}C$ in educational zones. The correlation between building coverage, green coverage, non-permeable pavement coverage, or floor area ratio(FAR) and surface temperature increases was also analyzed. The green coverage to temperature increases, but building coverage, non-permeable pavement coverage, and floor area ratio(FAR) had no statistically significant temperature increases. The factors that influence urban temperature changes were set up as independent variables and the surface temperature changes as dependent variables to construct a surface temperature change model for the land use types of former paddy fields. As a result of regression analysis, green coverage was selected as the most significant independent variable. According to regression analysis, if farmland is converted into an urban area, a temperature increase of $+3.889^{\circ}C$ is anticipated with 0% green coverage. The temperature saw a decrease of $-0.43^{\circ}C$ with every 10% increase of green coverage.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.253-261
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• 1996

In this paper, we derive the maximum likelihood estimator of P=P(X

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.523-532
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• 1997

We propose several estimators of the reliability function R of the two-parameter exponential distribution, and then compare those estimator in terms of the mean square error (MSE) through Monte Carlo method. We also consider the parametric bootstrap estimation. Using the parametric bootstrap estimator, we obtain the bootstrap confidence intervals for reliability function and compare the proposed bootstrap confidence intervals in terms of the length and the coverage probability through Monte Carlo method.

• KCID journal
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• v.9 no.1
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• pp.29-37
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• 2002

Rapid environmental changes result in not only damage to agricultural produce but also the poor growth and death of plants. Remote Sensing (RS) is one of effective methods for collecting, analyzing information and predicting the change of agricultural env