• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.145-159
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• 1996

We propose an adaptive searching method using some spatial relations among sample points to estimate the interesting area in the spatial population. The fundamental idea is to observe the neighboring sample points when a sample point is satified with some condition of an adaptive searching observation. For obseving the sample points with this method to estimate the area the sample size is decreased. From this result, we may expect to reduce the cost and time consuming in observation the sample points and to draw the shape of the interesting area without prior information of an spatial population. Some analytical simulation results are also presented.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.800-806
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• 2007

IT 벤처기업은 자본 대비 높은 수익성을 가지므로 지식기반 산업환경에서 많은 투자자들의 집중적인 관심을 받고 있다. 이러한 IT 벤처기업의 효율성을 평가하기 위한 방안으로, DEA 와 데이터마이닝 기법을 통합하는 방안을 제시하였다. 국내 코스닥 상장 기업 가운데 IT 에 주력하고 있는 벤처기업들을 대상으로 본 연구에서 제시한 효율성 평가방법을 적용 하였다. 대표적인 비모수적 분석기법인 Data Envelopment Analysis(DEA)를 이용하여 연구대상 기업들을 효율기업 및 비효율기업으로 구분한 후, DEA 의 효율성을 설명하는 모형을 logit 을 이용하여 구축하였다. DEA 는 기업의 상대적인 효율성을 측정하는 데에서 우수하지만, 효율성 정도를 설명하는 모형의 구축에는 한계가 있다. 이를 보완한 DEA 의 결과를 logit 과 통합한 효율성 모형에 대해서 데이터 마이닝 기법인 logit, 판별분석, Support Vector Machine(SVM) 등을 적용하여 IT 벤처기업의 효율성을 사전에 예측하여 평가 및 투자에 활용할 수 있는 방안을 제시하였다.

• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.45-53
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• 2000

본 논문에서는 모든 판정자(judge)들이 모든 객체(object)들에 대해 순위를 부여할 수 없는 경우에 얻어지는 불완전 순위자료에서 판정자들의 처리 효과에 대한 유의성을 검정하는데 관심이 있다. 이를 위해 불완전 순위자료를 완전자료로 바꾸는 알고리즘을 제안하고 알고리즘에 의해 얻어진 완전 순위자료에 Friedman 검정법을 적용하고자 한다. 제안된 검정법은 결측 객체에 순위를 부여하는데 있어서 완전순위를 갖는 판정자들의 정보를 이용함으로서 효율적이며 검정을 시행하는데 기존의 Friedman 통계량에 대한 분포표를 사용할 수 있어 간편하다. 그리고 몬테칼로 모의실험을 통하여 제안된 검정법과 기존의 평균 순위법, 최대/최소 Friedman 검정법과 검정력을 비교하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.117-123
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• 2006

최근 들어 전통적인 (가속)수명시험으로도 고 신뢰도 제품의 신뢰도 평가가 힘들므로 제품의 성능열화를 관측하여 수명 정보를 추정하는 열화 시험에 대한 관심이 증대되고 있다. 본 논문은 대수정규분포를 따르는 확률계수 열화율 모형 하에서 분포 모수 및 수명분포의 분위수를 추정하는 세 가지 통계적 분석법(근사적, 해석적, 수치적 방법)의 통계적 성능을 비교하였다. 즉, 다양한 수치실험상황 하에서 모형에 포함되는 (측정)오차의 영향을 고려하여 세 방법의 우월성을 조사하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.9-15
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• 2013

Recent works on ad hoc network study have shown that achievable throughput can be made to scale linearly with the number of receive antennas even if the transmitter has only a single antenna. In this paper, we propose a non-parametric linear minimum mean square error (MMSE) receiver for achieving further gain in performance when the channel state information at receiver (CSIR) of interferers is imperfect. The key feature to make our approach effective is to exploit the autocorrelation of the received signal. In fact, by incorporating the desired channel information on top of the observations including interference and noise only, the proposed method achieves large fraction of the optimal MMSE transmission capacity without transmission rate loss. From the SINR analysis as well as transmission capacity simulations in realistic ad hoc network system, we show that the proposed non-parametric linear MMSE receiver brings substantial performance gain over existing multiple receive antenna algorithms.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.487-494
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• 2019

Recently, interest in and use of drones is increasing. In this study, to provide accurate wind prediction at ultra low altitudes of 150 meters or below, the sensitivity of the physical process parameterization and initial conditions was assessed to select the optimal physical process and initial conditions. For this purpose, GFS and LDAPS data were used as initial and boundary conditions, and 7 experiments were constructed using a combination of PBL schemes such as YSU, RUC, ACM2, and LSM such as Noah, RUC, and Pleim. The experiment conducted for 1 month in April 2018. As a result, the RUC-YSU physical process combination using the GFS initial data showed the best performance. This study is meaningful in establishing an optimal modeling method for ultra low altitude wind prediction through experiments using different initial conditions and combination of physical processes.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.733-749
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• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.535-544
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• 2006

We consider use of Bootstrap calibration in the problem of setting a confidence interval for a linear combination of variance components. Based on the the modified large sample(MLS) method by Graybill and Wang(1980), Bootstrap Calibration is applied to improve the coverage probability of the MLS confidence bound when the experiment is balanced and coefficients of a linear combination are positive. Performance of the proposed confidence bound in small sample is investigated by simulation studies.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.567-575
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• 2016

With the popularity of smartphone, user's location information is of great interest as mobile apps based on the location information are increasing. In this paper, we are interested in analyzing user's speed data based on the location information. It is not uncommon to observe locations with great measurement errors, removing them is necessary. The distribution of speed can be considered as a mixture model in accordance with transportation means. We identify a tail part as a component of a mixture model and fit a simple parametric model to the tail part of the speed distribution.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.341-353
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• 2013

In many practical machine learning and data mining applications, unlabeled data are inexpensive and easy to obtain. Semi-supervised learning try to use such data to improve prediction performance. In this paper, a semi-supervised regression method, semi-supervised kernel ridge regression estimation, is proposed on the basis of kernel ridge regression model. The proposed method does not require a pilot estimation of the label of the unlabeled data. This means that the proposed method has good advantages including less number of parameters, easy computing and good generalization ability. Experiments show that the proposed method can effectively utilize unlabeled data to improve regression estimation.