• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.521-534
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• 2006

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

• 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.

• Environmental Analysis Health and Toxicology
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• v.24 no.2
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• pp.79-93
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• 2009

오염물질에 대한 생태위해성평가(ecological risk assessment)를 위해서는 노출평가(exposure assessment)와 함께 생물영향에 대한 평가(effect assessment)를 수행해야 한다. 노출평가의 경우는 지화학적 과정에 대한 이해를 바탕으로 환경농도를 예측하기 위한 화학평형모델이나 다매체환경거동모델 등 다양한 평가 및 예측모델을 활용해 왔다. 이와 달리 생물영향평가는 실험실 조건에서 제한된 독성자료를 대상으로 외부노출농도에 기반한 농도-반응관계를 통계적 방법을 통해서 추정하는 '경험적 모델(empirical model)'에 주로 의존해 왔다. 최근에 와서 생체 내 잔류량을 기반으로 농도-시간-반응관계를 기술하고 예측하는 독성동태학 및 독성역학 모델(toxicokinetic-toxicodynamic model)과 같은 독성작용에 기반한 모델(processbased model)들이 개발되어 활용되고 있다. 본 논문에서는 여러 종류의 독성동태학 및 독성역학 모델을 소개하고, 이를 통계적 추론에 기반한 전통적인 독성학 모델과 비교하였다. 서로 다른 종류의 독성동태학 및 독성역학 모델로부터 도출된 노출농도-시간 -반응관계식을 비교하고, 동일 독성기작을 보이는 오염물질 그룹 내에서 미측정 오염물질의 독성을 예측할 수 있게 해주는 구조-활성관계(Quantitative Structure-Activity Relationship, QSAR) 모델을 여러 독성동태 및 독성역학모델로부터 유도하였다. 마지막으로 독성동태학 및 독성역학 파라미터를 추정하기 위한 실험계획을 제안하였고, 앞으로 독성동태학 및 독성역학 모델을 생태계 위해성평가에 활용하기 위해서 해결해야 될 연구과제를 검토하였다.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.5
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• pp.426-434
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• 2011

Accurate weight prediction methods are an essential of the ship design in both ship cost managements and performance satisfactions. When no parent or similar ships are available, an adequate method of the ship weight estimating is required. In this study, there was carried out to develop the ship weight estimating method for the preliminary design phase. The weight estimating methods were first surveyed by the references and summarized their characteristics. The weight estimation method by statistical approach was developed for the container ship because the containerized transportation markets is gradually growing and ship's size and loading capacity are rapidly enlarged. The correlation analysis and the multiple regression analysis were used for developing the weight estimating method. As a results of evaluating the developed method, the error ratio of the variation between estimated weight and ship's data was about 5%. And it was only 1% difference with the calculating weight of conceptual design results by shipyard design team that the estimating weight of ultra-large container ship was predicted by the developed method.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.39-46
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• 2017

In this paper, we proposes method of vanishing point detection using orthogonality of vanishing point, under the "Manhattan World" assumption that the structure of the city is mostly grid and vanishing point are orthogonal to each other. The feature that the vanishing point are orthogonal to each other can be useful for inferring the missing point that are not detected among the three vanishing point, and prevent the vanishing point detected close to the other vanishing point. In this paper, we detect Vertical vanishing point through statistical approach and detect Horizontal and Front vanishing point through structural approach. Experimental results show that the proposed method improves the detection accuracy of the vanishing point compared with the existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.592-599
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• 2015

In big data environment, we need new approach for big data analysis, because the characteristics of big data, such as volume, variety, and velocity, can analyze entire data for inferring population. But traditional methods of statistics were focused on small data called random sample extracted from population. So, the classical analyses based on statistics are not suitable to big data analysis. To solve this problem, we propose an approach to efficient big data analysis. In this paper, we consider a big data analysis using principal component analysis, which is popular method in multivariate statistics. To verify the performance of our research, we carry out diverse simulation studies.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.84-90
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• 2009

In this paper, the new channel estimation was proposed that have the low complexity and high performance using Fuzzy inference method uses recently from various field for estimation about uncertainty in channel estimation of OFDM. Proposed method is channel estimation performance improve, calculation and interpolation for statistics character of channel using the pilot before LS channel estimation by Fuzzy inference method. Simulation result in QPSK proposed channel estimation method shows the enhancement of 5.5dB compared to the LS channel estimation and the deterioration of 1.3dB compared to the MMSE channel estimation in mean square error point $10^{-3}$. symbol error rate shows similarity performance the MMSE $10^{-1.96}$, proposed channel estimation $10^{-1.93}$ and enhancement of $10^{-0.35}$ compared to the LS channel estimation in signal to noise ratio point 20dB.

• Korean Journal of Construction Engineering and Management
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• v.9 no.6
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• pp.257-267
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• 2008

In order to encourage construction practitioners to acknowledge failures and disseminate the information, the failure information must be documented and accumulated with a well-structured format, which contains not only the fact and result but also the circumstance and cause of the failure. In the Korean construction industry, many failures are not explained clearly and often not even reported publicly, partly because due to the lack of understanding positive aspects of failures, which can improve construction practices as a result of learning from failures. The purpose of this study is to develop a web-based construction failure information system using the case-based reasoning techniques, which can systematically accumulate, manage, and share the valuable failure information using a structured failure cases database. It can be utilized for planning proactive solutions on future failures by searching the very similar past failure cases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1693-1705
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• 2013

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.615-632
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• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.