• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.515-525
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• 2010

We investigated design-based properties of the ordinary least square estimator(OLSE) and the weighted least square estimator(WLSE) in a panel regression model. Given a complex data we derive the magnitude of the design-based bias of two estimators and show that the bias of WLSE is smaller than that of OLSE. We also conducted a simulation study using Korean welfare panel data in order to compare design-based properties of two estimators numerically. In the study we found the followings. First, the relative bias of OLSE is nearly two times larger than that of WLSE and the bias ratio of OLSE is greater than that of WLSE. Also the relative bias of OLSE remains steady but that of WLSE becomes smaller as the sample size increases. Next, both the variance and mean square error(MSE) of two estimators decrease when the sample size increases. Also there is a tendency that the proportion of squared bias in MSE of OLSE increases as the sample size increase, but that of WLSE decreases. Finally, the variance of OLSE is smaller than that of WLSE in almost all cases and the MSE of OLSE is smaller in many cases. However, the number of cases of larger MSE of OLSE increases when the sample size increases.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.457-459
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• 2004

본 논문에서는 NHPP에 기초한 여러 기존 소프트웨어 신뢰도 모델들이 가중 산술, 가중 기하, 또는 가중 조화평균의 개념을 적용하여 어떻게 유도되는가를 기술한다. 그 외에도, 이러한 3개의 가중치 평균에 근거하여 유사산술의 관점으로부터 좀더 일반적인 NHPP 모델을 제안한다. 상기 3개 평균 외에 변환의 파라미터 계열을 포함한 좀더 일반적인 변환을 공식화한다. 이러한 일반적인 프레임웍 하에서 기존의 NHPP를 입증하고 여러 가지 새로운 NHPP클 유도한다. 우리는 이러한 접근법들이 상이한 조건 하의 여러 가지 잘 알려진 모델들을 포함하는 것으로 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.947-951
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• 2010

본 연구는 강우시 A 공업단지와 B 농공단지의 공업지역에서 통계적인 오염물질 농도와 오염물질 특성을 찾아내기 위하여 모니터링 및 분석을 수행하였으며, 강우유출수 조사방법에 따른 원단위 산정을 위하여 유량가중평균농도(EMC)산정, 강우계급별 유량가중평균농도를 산정하였으며, 공업지역의 대표 유량가중평균농도(EMCz) 산정, 유출율을 산정하였으며, 앞에 산정한 자료를 이용하여 선정한 공업지역 원단위를 산정하여 기존 원단위 값과 비교분석 하였다. 이렇게 산출된 자료는 공업지역 비점오염원 최적관리를 위한 과학적 근거자료 제공 및 기초자료서의 활용, 모니터링을 통한 공업지역의 비점오염원의 관리대책 및 낙동강 수질개선을 위한 정책자료에 관한 기초자료 제공, 국내 실정에 부합하는 최적 비점오염원 저감시설의 설치를 위한 기초자료로 활용될 수 있을 것이다.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.233-241
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• 1999

Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, we propose a robust EWMA(exponentially weighted moving averages) control chart for variables, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a s table level and high power. To compare the performances of the proposed control chart with other charts, some Monte Carlo simulations we performed. The simulation results show that the robust EWMA control chart has good performance.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.43-49
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• 2023

Object tracking is being studied with various techniques such as Kalman filter and Luenberger tracker. Even in situations, such as the one in which the system model is not well specified, to which existing signal processing techniques are not successfully applicable, it is possible to design artificial neural networks to track objects. In this paper, we propose an artificial neural network, which we call 'maximum-likelihood weighted-average neural network', to continuously track unpredictably moving objects. This neural network does not directly estimate the locations of an object but obtains location estimates by making weighted average combining various results of maximum likelihood tracking with different data lengths. We compare the performance of the proposed system with those of Kalman filter and maximum likelihood object trackers and show that the proposed scheme exhibits excellent performance well adapting the change of object moving characteristics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.4
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• pp.323-329
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• 2004

Water vapor is an important parameter in monitoring changes in the Earth's climate and it can be used to improve weather forecasting. However, it haven't observed accurately by reasons of structural and economic problem of observation. GPS meteorology technique for precipitable water vapor measurement is currently actively being researched an advanced nation. Main issue of GPS meteorology is an accuracy of PWV measurement related weighted mean temperature and meteorological data. In this study, the korean weighted mean temperature had been recalculated by a linear regression method based on meteorological observations from 6 radiosonde stations for 2003 year. We examined the accuracy of PWV estimates from GPS observations and Radiosonde observations by new korean weighted mean temperature and others.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.565-574
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• 2009

The water vapor weighted vertically mean temperature(Tm) models, which were developed by the consideration of seasonal characteristics over the Korea, was used in the retrieval of precipitable water vapor (PWV) from GPS data which were observed at four GPS permanent stations. Since the weighted mean temperature relates to the water vapor pressure and temperature profile at a site, the accuracy of water vapor information which were estimated from GPS tropospheric wet delay is proportional to the accuracy of the weighted mean temperature. The adaption of Korean seasonal weighted mean temperature model, as an alternative to other formulae which are suggested from other nation, provides an improvement in the accuracy of the GPS PWV estimation. Therefore, it can be concluded that the seasonally appropriate weighted mean temperature model, which is used to convert actual zenith wet delay (ZWD) to the PWV, can be more reduced the relative biases of PWV estimated from GPS signal delays in the troposphere than other annual model, so that it would be useful for GPS PWV estimation with high accuracy.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.447-455
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• 2000

We propose a weighted kernel pattern recognition method using the K -means clustering algorithm to reduce computation and storage required for the full kernel classifier. This technique finds a set of reference vectors and weights which are used to approximate the kernel classifier. Since the hierarchical clustering method implemented in the 'Weighted Parzen Window (WP\V) classifier is not able to rearrange the proper clusters, we adopt the K -means algorithm to find reference vectors and weights from the more properly rearranged clusters \Ve find that the proposed method outperforms the \VP\V method for the repre~entativeness of the reference vectors and the data reduction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.97-105
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• 2015

Multiresponse optimization (MRO) seeks to find the setting of input variables, which optimizes the multiple responses simultaneously. The approach of weighted mean squared error (WMSE) minimization for MRO imposes a different weight on the squared bias and variance, which are the two components of the mean squared error (MSE). To date, a weighted sum-based method has been proposed for WMSE minimization. On the other hand, this method has a limitation in that it cannot find the most preferred solution located in a nonconvex region in objective function space. This paper proposes a Tchebycheff metric-based method to overcome the limitations of the weighted sum-based method.

• Journal of the Korean Society for information Management
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• v.30 no.4
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• pp.241-264
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• 2013

This study compared and analyzed weighted network centrality measures supported by Opsahl's tnet and Lee's WNET, which are free softwares for weighted network analysis. Three node centrality measures including weighted degree, weighted closeness, and weighted betweenness are supported by tnet, and four node centrality measures including nearest neighbor centrality, mean association, mean profile association, triangle betweenness centrality are supported by WNET. An experimental analysis carried out on artificial network data showed tnet's high sensitiveness on linear transformations of link weights, however, WNET's centrality measures were insensitive to linear transformations. Seven centrality measures from both tools, tnet and WNET, were calculated on six real network datasets. The results showed the characteristics of weighted network centrality measures of tnet and WNET, and the relationships between them were also discussed.