• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.859-862
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• 2004

유역평균강우량은 강우-유출모형을 통하여 유출량을 산정할 경우에 사용되며, 산정 방법에는 산술평균법, Thiessen 가중법, 등우선법 등이 있으나 일반적으로 Thiessen 가중법을 많이 적용하고 있다. Thiessen 가중법의 유역평균 강우량 산정방법은 각 관측소가 지배하는 면적(지배면적)을 전체면적으로 나누어 가중치(Thiessen계수)를 구한 후 여기에 각 관측소의 강우량을 곱하고 이를 합산함으로써 유역평균 강우량을 산정하는 방법이다. 본 연구에서는 면적비로 구해지는 Thiessen 계수의 대안을 찾기 위해 대상 유역으로는 영산강 1지류인 지석천 유역을 선정하였고, 단층퍼셉트론을 이용하여 동면, 청풍, 능주의 강우자료를 Input, 능주지점의 유출자료를 Output으로 상호 상관분석으로부터 한 개의 유출 사상에 대해 가장 높은 상관계수를 선택하여 Input 자료를 재구성하였다. 재구성 한 자료를 이용하여 훈련시키고 여기서 발생한 가중치를 Thiessen 계수의 대안의 값으로 추천한다.

• Geophysics and Geophysical Exploration
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• v.5 no.3
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• pp.169-177
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• 2002

We develop the weighted-averaging finite-element method which uses four kinds of element sets. By constructing global stiffness and mass matrices for four kinds of element sets and then averaging them with weighting coefficients, we obtain a new global stiffness and mass matrix. With the optimal weighting coefficients minimizing grid dispersion and grid anisotropy, we can reduce the number of grid points required per wavelength to 4 for a $1\%$ upper limit of error. We confirm the accuracy of our weighted-averaging finite-element method through accuracy analyses for a homogeneous and a horizontal-layer model. By synthetic data example, we reconfirm that our method is more efficient for simulating a geological model than previous finite-element methods.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.529-537
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• 2006

This paper investigates the impacts of the drag-related weighting coefficients on mean velocity and turbulence structures. The transport equations for the Reynolds stress of vegetated open-channel flows are derived by using the temporal- and horizontal-averaging scheme. It is found that the total Reynolds stress of vegetated open channel flows consists of the Reynolds stress due to temporally fluctuating velocities and the Reynolds stress due to spatially fluctuating velocities. The drag-related weighting coefficient $C_{fk}$ for the total Reynolds stress component is found to be unit, while the coefficient for the Reynolds stress due to temporally fluctuating velocities can be negligible. This is the reason why very small weighting coefficients in previous studies yield very good agreements with measured data. In other words, the Reynolds stress due to spatially fluctuating velocities remains still unknown, especially due to the large number of measuring locations. Through a developed Reynolds stress model, vegetated open-channel flows are simulated and compared with measured data from the literature. Comparisons reveal that the computed mean flow and Reynolds stress structures are hardly affected by the drag-related weighting coefficients. However, the computed turbulence intensity profiles are significant different with the drag-related weighting coefficients. A budget analysis of the transport equations for the Reynolds stress component is carried to investigate why turbulence intensity is affected by the drag-related weighting coefficients.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.395-407
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• 2007

Outliers distort many measures for data analysis. We can propose dandelion seed plot as a graphical tool for evaluating the effect of outliers in one-and two-variate data. We can draw mean-variance dandelion seed plots using linked curves which are made by changing weights from 1 to 0 for each datum. Similarly we can also draw covariance-correlation-coefficient dandelion seed plots. This graphical method can be a useful tool for elementary statistics education in college.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.681-688
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• 2010

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting scheme in practice. And EWMA control chart is used when it is desirable to detect out-of-control situations very quickly because of sensitive to a small or gradual drift in the process.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.395-403
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• 2002

In the third phase of the response surface methods, the first-order model is assumed and the curvature of the response surface is checked with a fractional factorial design augmented by centre runs. We further assume that a true model is a quadratic polynomial. To choose an optimal design, Box and Draper(1959) suggested the use of an average mean squared error (AMSE), an average of MSE of y(x) over the region of interest R. The AMSE can be partitioned into the average prediction variance (APV) and average squared bias (ASB). Since AMSE is a function of design moments, region moments and a standardized vector of parameters, it is not possible to select the design that minimizes AMSE. As a practical alternative, Box and Draper(1959) proposed minimum bias design which minimize ASB and showed that factorial design points are shrunk toward the origin for a minimum bias design. In this paper we propose a robust AMSE design which maximizes the minimum efficiency of the design with respect to a standardized vector of parameters.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.45-56
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• 2002

In this research we estimated regional gross fixed capital stock of transport sector, such as road railroad, airport and seaport during 1968-1997 in Korea. We also compared our estimation results with those of Korea and Japan. As basic analytic method, we used the regional allocation method. To estimate regional gross fixed capital stock of transport sector, we used the basic data on national wealth surveys in 1997, regional land price index and regional facilities index in transport sectors. We used the most reasonable data in the process of estimation after reviewing the collected data In order to get the reasonable capital stock by regions. we chose the allocation index which can minimize the difference between the estimated result and the real regional capital stock in the process to allocate the total gross capital into the regions. Compared our results with those of other researches in Korea, estimates in our research project could be said more accurate than those.

• Journal of Korea Water Resources Association
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• v.42 no.2
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• pp.131-139
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• 2009

The weather accidents by global warming effect are increasing rapidly whole world. Flood forcasting system and hydrological database are operated by almost all the countries in the world. An objective of this study is to research revised methods of missing rainfall data and find more effective revised method for this operating system. 194 rainfall data of the Han river basin is used. Arithmetic average method, coefficient of correlation weighting method and inverse distance weighting method are compared to estimate revised methods. The result from the analysis shows that coefficient of correlation weighting method is best quantitatively among the 3 methods.