• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.149-160
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• 2015

This study aims to estimate the forest volumes of the upper region of Nam-Han River in ecosystem zoning by forest types and age classes, and to suggest the optimal estimation method through the comparison of the standard errors according to the spatial unit. In the estimation of forest volumes, we used both of direct estimation, which uses sample plots of the target area only, and synthetic estimation, which includes sample plots of the expanded areas as well as those of the target area. As for the spatial expansion, we applied four standards for synthetic estimator: Mountainous zone, Neighbor ecosystem region, Gangwon province, and Buffer zone. The results show that average forest volume per ha, calculated by direct estimation, was $143.5m^3/ha$, while that by synthetic estimation with each standard, was estimated at $146.9m^3/ha$ by Gangwon province, $144.8m^3/ha$ by Buffer zone, $139.8m^3/ha$ by Neighbor ecosystem region, and $138.6m^3/ha$ by Mountainous zone, respectively. The standard errors of direct estimation was $1.79m^3/ha$, while those of synthetic estimation showed not a great difference among the errors. Meanwhile, considering the standard errors by forest type, the lowest was ${\pm}2.3m^3/ha$ of broad-leaved forest, followed by ${\pm}3.3m^3/ha$ of mixed forest, and ${\pm}4.8m^3/ha$ of coniferous forest.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.23-30
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• 2011

In the wireless sensor network(WSN), the detection of precise location of sensor nodes is essential for efficiently utilizing the sensing data acquired from sensor nodes. Among various location methods, the received signal strength (RSS) based localization scheme is mostly preferable in many applications since it can be easily implemented without any additional hardware cost. Since the RSS localization method is mainly effected by radio channel between two nodes, outlier data can be included in the received signal strength measurement specially when some obstacles move around the link between nodes. The outlier data can have bad effect on estimating the distance between two nodes such that it can cause location errors. In this paper, we propose a RSS-based localization method using Robust Statistic and Gaussian filter algorithm for enhancing the accuracy of RSS-based localization. In the proposed algorithm, the outlier data can be eliminated from samples by using the Robust Statistics as well as the Gaussian filter such that the accuracy of localization can be achieved. Through simulation, it is shown that the proposed algorithm can increase the accuracy of localization and is more robust to non gaussian noise channels.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.13-22
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• 2012

Cut-off sampling that discards a part of the population from the sampling frame, is a widely used method for a business survey. Usually, to the estimate of population total, an accurate estimate of the total of the take-nothing stratum is required. Many estimators have been developed to estimate the total of the take-nothing stratum. Recently Kim and Shin (2011) suggested a composite estimator and showed the superiority of that estimator. In this paper, we suggest an alternative composite estimator obtained by combining BLUP estimator and a ratio estimator obtained by the small samples from the take-nothing stratum. Small simulation studies are performed for a comparison of the estimators and we confirm that the new suggested estimator is superior.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.135-145
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• 2008

As a fundamental input to the travel demand forecasting, OD has been always a concern in obtaining the accurate link traffic volume. Numerous methods were applied thus far without a complete success. Some existing OD estimation techniques generally extract regular samples and expand those sample into population. These methods, however, leaves some to be desired in terms of accuracy. To complement such problems, research on estimating OD using additional information such as link traffic volume as well as sample link use rate have been accomplished. In this paper, a new approach for estimating static origin-destination (OD) using probe vehicle has been proposed. More specifically, this paper tried to search an effective sample rate which varies over time and space. In a sample test network study, the traffic volume error rate of each link was set as objective function in solving the problem. As a key result the MAE (mean absolute error) between expanded OD and actual OD was identified as about 5.28%. The developed methodology could be applied with similar cases. Some limitations and future research agenda have also been discussed.

• Survey Research
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• v.8 no.2
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• pp.91-115
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• 2007

OES survey as the national official statistics aims to provide the basic data for the national labor market policy and research such as the basic statistics for human resource supply policy, the prediction of employment by occupations, the decision of occupation, the occupational training and the finding jobs et al., at the levels of industrial and occupational classifications(3-digit). In order to achieve this objective, we analyze the OES data in 2005 and 2006 and propose the new sampling design using the long form data in Korea (10% sample data of census 2005). In this paper, we provide the criterion of sample allocation and derive the formular for estimator and error of it including the weighting procedure. From the proposed sampling design, we would expect that it contributes to the supply policy of human resource and the research for labor market.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.659-668
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• 2006

The uncertainty of the risk of failure of hydraulic structures can be determined by estimating the variance of the risk of failure based on the methods of moments, probability weighted moments, and maximum likelihood assuming that the underlying model is the Gumbel distribution. In this paper, the variance of the risk of failure was derived. Monte Carlo simulation was peformed to verify the characteristics of the derived formulas for various sample size, design life, nonexceedance probability, and variation coefficient. As the results, PWM showed the smallest relative bias and root mean square error than the others while ML showed the smallest ones for relatively large sample siBes regardless of design life and nonexceedance probability. Also, it was found that variation coefficient does not effect on the relative bias and relative root mean square error.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.4
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• pp.433-442
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• 2010

In this paper, self-calibration algorithm based on covariance matrix is proposed for compensating amplitude/phase mismatch in multi-baseline interferometer direction finding system. The proposed method is a solution to nonlinear constrained minimization problem which dramatically calibrate mismatch error using space sector concept with cost function as defined in this paper. This method, however, has a drawback that requires an estimated initial angle to determine the proper space sector. It is well known that this type of drawback is common in nonlinear optimization problem. Superior calibration capabilities achieved with this approach are illustrated by simulation experiments in comparison with interferometer algorithm for a varitiety of amplitude/phase mismatch error. Furthermore, this approach has been found to provide an exceptional calibration capabilities even in case amplitude and phase mismatch are more than 30 dB and over $5^{\circ}$, respectively, with sector spacing of less than $50^{\circ}$.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.341-353
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• 1993

As an estimator of the conditional probability of discovering a new species at the next observation after a sample of certain size is taken, the one proposed by Good(1953) has been most widely used. Recently, Clayton and Frees(1987) showed via simulation that their nonparametric maximum likelihood estimator(NPMLE) has smaller MSE than Good's estimator when the population is relatively nonuniform. Lee(1989) proved that their conjecture is asymptotically true for truncated geometric population distributions. One shortcoming of the NPMLE, however, is that it has a considerable amount of negative bias. In this study we proposed a bias-corrected version of the NPMLE for virtually all realistic population distributions. We also showed that it has a smaller asymptotic MSE than Good's extimator except when the population is very uniform. A Monte Carlo simulation was performed for small sample sizes, and the result supports the asymptotic results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.411-417
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• 2017

It is necessary to determine the current and future energy consumption by farm households for the rational specification of energy related policy in the Korean agricultural sector. Especially, It is important to identify the consumption by source of energy and by the crops. On the other hand, the world has tried to reduce the production of greenhouse gases and, in line with this, the Korean government established related legislations to contribute to this reduction (30% reduction in emissionsby 2020). The reduction target of the agricultural sector is specified as 5.2% of the national total. This study focuses on sampling design to determine the energy consumption and emission of greenhouse gases, and suggests several alternatives to improve the confidence level and to make a dent survey and estimation errors. The population for the energy consumption survey of the agricultural sector was derived from agricultural census data. In the case of commodities with high skewness, we cut the sample range to within the statistical significant range. The number of samples in each class is specified using the Neyman allocation method and 95% significance level. The estimation results are compared with the population to verify the statistical significance and several management methods of sampling errors are suggested.