• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.149-154
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• 2004

이 논문에서는 표본조사에서 자주 사용되는 표본의 대표성, 비편향성, 그리고 효율성에 개넘에 대하여 고찰하였다. 표본의 대표성은 조사단위의 포함확률로 표현되며 조사모집단의 포함범위와 연관이 있는 반면, 비편향성과 효율성은 표집설계와 추정량에 관련된 개념이다. 비편향성과 효율성은 표본의 대표성을 전제로 하며 가중치 부여로 나타난다

• Survey Research
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• v.6 no.1
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• pp.39-62
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• 2005

In this paper we investigate some concepts frequently called in sample surveys such as 'representative of sample' as well as 'consistency', 'unbiasedness', and 'efficiency' in estimation. The first is strongly related with sampling procedure including coverage rate of survey population, response rate in establishment survey, and recruit rate of final samples. The others, however, are concerned with both sampling design and corresponding estimators simultaneously. Whereas both consistency and unbiasedness are based on the representative sample, efficiency does not depend on the representative sample. The representative of sample can be increased by raising the rate of coverage, response and recruit as well. Consistency may be investigated according to variables of interest and auxiliary variables. The well-known raing-ratio weighting method is a method to increase consistency of auxiliary variables by means of matching population size in each cell. Efficiency is not directly related with the representative of sample, and allocation methods such as proportional and Neyman allocation in stratified sampling and post-stratification are all methods to increase the efficiency of estimation under the condition of satisfying the representative of sample.

• Proceedings of the Korean Society for Information Management Conference
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• 2001.08a
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• pp.167-172
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• 2001

클러스터링 성능 평가를 위한 척도로 여러 공식이 개발되어 사용되어왔다. 이들 평가척도는 가급적 범위가 제한되며, 가환성이 있고, 비편향적이며 단일 척도일 필요가 있다. 기존 평가 척도에 대해서 검토한 후 비편향적인 단일 척도 WACS를 개발하였다. 클러스터 수를 달리하는 클러스터링 결과에 대해 여러 평가척도를 적용해서 성능을 평가하는 실험을 통해서 WACS 척도가 평가척도로서의 요건을 만족시킨다는 것을 확인하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.3
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• pp.303-314
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• 2010

A study for the nonlinear analysis method of prestressed concrete(PSC) girders with external tendons is presented. The PSC girders with external tendons show the complex nonlinear behavior due to the slip of external tendons at deviator and the change of eccentricity between the girders and external tendons. The external tendon between anchorage-deviator or deviator-deviator is modeled as an assemblage of the curved elements. The slip effect of the external tendon at deviator is taken into account using the force equilibrium relationship between the friction force and the driving force at each deviator. The finite element model and analysis method of the external tendon suggested herein are integrated in the nonlinear analysis program of segmentally erected PSC frames developed by the authors. The proposed analysis method is verified through the comparison of the analysis and experimental results obtained from other investigators. From the ultimate analysis results of PSC beams with external tendons having different number of deviators, the yielding and ultimate loads of PSC beams found to be increased as the number of deviators are increased. In addition, the ultimate capacity of the PSC beam increases according to the increase of friction coefficient between deviator and external tendon, whereas found to decease over the certain value of friction due to the effect of the moment transmitted to the member by the friction force exerted from the external tendon.

• Korean Journal of School Psychology
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• v.17 no.3
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• pp.263-286
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• 2020

The purpose of this study was to explore how overly positive self-estimations in peer relationships relate to subjective well-being and to the occurrence of interpersonal behaviors supporting basic psychological needs among elementary school students. This study tested the optimal margin hypothesis of positive illusion by examining the curvilinear relationship between these variables. The sample consisted of 346 fifth and sixth grade students. The self-criterion residual method was used to derive self-estimation bias scores by regressing the real peer relations index (i.e., In-degree) on their perceived peer relationship qualities. The results showed that girls more strongly overestimated the quality of their peer relationships than boys. Self-estimation biases had a positive curvilinear relationship with negative affects and a negative curvilinear relationship with relatedness needs supporting interpersonal behaviors. These results supported the existence of the optimal margin of positive illusion because overestimations of the quality of peer relationships were associated with lower levels of negative affects and relatedness needs-supporting interpersonal behaviors, though these benefits flattened out and no further benefit was observed after an optimal level of overestimation. However, self-estimation bias was linearly associated with positive affect, autonomy needs-supporting interpersonal behaviors, and competence needs-supporting interpersonal behaviors. These results indicated that optimal margin hypothesis was not supported for all outcome variables.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.53-58
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• 2021

In this paper, an alternative finite memory structure(FMS) smoothing filter is developed for discrete-time state-space model with a control input. To obtain the FMS smoothing filter, unbiasedness will be required beforehand in addition to a performance criteria of minimum variance. The FMS smoothing filter is obtained by directly solving an optimization problem with the unbiasedness constraint using only finite measurements and inputs on the most recent window. The proposed FMS smoothing filter is shown to have intrinsic good properties such as deadbeat and time-invariance. In addition, the proposed FMS smoothing filter is shown to be equivalent to existing FMS filters according to the delay length between the measurement and the availability of its estimate. Finally, to verify intrinsic robustness of the proposed FMS smoothing filter, computer simulations are performed for a temporary model uncertainty. Simulation results show that the proposed FMS smoothing filter can be better than the standard FMS filter and Kalman filter.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.79-89
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• 2007

A test for normality introduced by Arizono and Ohta(1989) is based on fullback-Leibler discrimination information. The test statistic is derived from the discrimination information estimated using sample entropy of Vasicek(1976) and the maximum likelihood estimator of the variance. However, these estimators are biased and so it is reasonable to make use of unbiased estimators to accurately estimate the discrimination information. In this paper, Arizono-Ohta test for normality is improved. The derived test statistic is based on the bias-corrected entropy estimator and the uniformly minimum variance unbiased estimator of the variance. The properties of the improved KL test are investigated and Monte Carlo simulation is performed for power comparison.

• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.118-127
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• 2010

Resistivity logging instruments are designed to measure the electrical resistivity of a formation, and this can be directly interpreted to provide a water-saturation profile. However, resistivity logs are sensitive to borehole and shoulder-bed effects, which often result in misinterpretation of the results. These effects are emphasised more in the presence of tool eccentricity. For precise interpretation of short- and long-normal logging measurements in the presence of tool eccentricity, we simulate and analyse eccentricity effects by combining the use of a Fourier series expansion in a new system of coordinates with a 2D goal-oriented high-order self-adaptive hp finite-element refinement strategy, where h denotes the element size and p the polynomial order of approximation within each element. The algorithm automatically performs local mesh refinement to construct an optimal grid for the problem under consideration. In addition, the proper combination of h and p refinements produces highly accurate simulations even in the presence of high electrical resistivity contrasts. Numerical results demonstrate that our algorithm provides highly accurate and reliable simulation results. Eccentricity effects are more noticeable when the borehole is large or resistive, or when the formation is highly conductive.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.273-284
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• 2022

The goal of this paper is to propose the use of machine learning platforms in education to train learners to recognize data biases. Learners can cultivate the ability to recognize when learners deal with AI data and systems when they want to prevent damage caused by data bias. Specifically, this paper presents a method of data bias education using MachineLearningforKids, focusing on the case of AI baseball referee. Learners take the steps of selecting a specific topic, reviewing prior research, inputting biased/unbiased data on a machine learning platform, composing test data, comparing the results of machine learning, and present implications. Learners can learn that AI data bias should be minimized and the impact of data collection and selection on society. This learning method has the significance of promoting the ease of problem-based self-directed learning, the possibility of combining with coding education, and the combination of humanities and social topics with artificial intelligence literacy.