• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.382-386
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• 2012

최근 산지하천 유역에서 발생하는 홍수와 이를 동반한 토석류에 의해 많은 인적, 물적 피해가 빈번히 발생하고 있다. 이러한 피해를 최소화하기 위해서는 유역의 정확한 홍수유출량 해석이 동반되어야 하지만 산치하천 유역은 유출특성 분석에 기본이 되는 수위관측소의 수가 적고, 관측소가 존재하더라도 결측치가 많거나 자료보유 연한이 짧아 자료의 활용성이 떨어진다. 따라서 선행 연구에서는 미비한 자료만으로도 회귀분석이 가능하며 높은 신뢰도를 갖는 Fuzzy 회귀분석 기법을 도입하여 수위자료 없이도 산지하천 유역의 유역면적과 하도경사를 바탕으로 홍수유출량을 평가할 수 있는 기술을 개발하였다. 본 연구에서는 여기에 빈도별 강우량을 새롭게 추가하여 홍수량 산정식을 개선 및 보완하였다. 새롭게 도출된 홍수량 산정식의 정확도는 기존 대상유역 내 특정지점 설계홍수량을 기준으로 기존 개발된 홍수량 산정식과 비교하여 검토하였고 비교적 높은 정확도를 나타냈다. 이를 바탕으로 일반 사용자도 손쉽게 홍수량을 산정할 수 있도록 MATLAB을 이용하여 홍수량 산정 프로그램을 개발하였다.

• Korean Security Journal
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• no.39
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• pp.217-239
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• 2014

The purpose of this study is to compare with three juvenile delinquency theories on adolescents' status offenses including Hirschi's social bonding theory, Agnew's general strain theory, and Akers' social learning theory. The data derived from a sample of 2,337 middle school students taken from National Youth Policy Institute in 2011-2012. Multiple OLS regression analysis revealed that variables from social learning theory were strongly supported as an explanation for adolescents' status offenses, while variables from general strain theory were not supported. The social learning model explained 12.0% of the variance in adolescents' status offenses. However, general strain variables explained 2.6% of the variance in the dependant variable and 6.2% of the variance in adolescents' status offenses were explained by the social bonding variables. The present research made important contributions the further utilization of social learning in investigating many of the damaging forms of social deviance which exist in our society.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.121-132
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• 2007

Up to now Permanent traffic volumes have been counted by Automatic Vehicle Classification (AVC) on National Highways. When counted data have missing items or errors, the data must be revised to stay statistically reliable This study was carried out to estimate correct data based on outoregression and seasonal AutoRegressive Integrated Moving Average (ARIMA). As a result of verification through seasonal ARIMA, the longer the missed period is, the greater the error. Autoregression results in better verification results than seasonal ARIMA. Traffic data is affected by the present state mote than past patterns. However. autoregression can be applied only to the cases where data include similar neighborhood patterns and even in this case. the data cannot be corrected when data are missing due to low qualify or errors Therefore, these data shoo)d be corrected using past patterns and seasonal ARIMA when the missing data occurs in short periods.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.75-81
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• 2016

This study focused on exploring real transaction price of apartment and spatial and temporal heterogeneity of the variables that influence real transaction price of apartment from the spatial and temporal perspective. As independent variables that are considered to influence real transaction price of apartment, transport, local characteristics, educational conditions, population, and economic characteristics were taken into account. Accordingly, the influence of independent variables and spatial distribution pattern were analyzed from the global and local aspects. The spatial and temporal changing patterns of real transaction price of apartment which is a dependent variable were analyzed. First, to establish an analysis model, OLS analysis and GWR analysis were conducted, and thereby more efficient and proper model was selected. Secondly, to find spatial and temporal heterogeneity of independent variables with the use of the selected GWR model, Local $R^2$ was used for local analysis. Thirdly, to look into spatial distribution of independent variables, kriging analysis was carried out. Therefore, based on the results, it is considered that it is possible to carry out more microscopic housing submarket analysis and lay the foundation for establishing a policy on real property.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.157-169
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• 2016

We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of the Korean Geotechnical Society
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• v.29 no.11
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• pp.99-106
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• 2013

In this study, the correlation of evaluation items and safety rating for 104 of large-scale slopes along the general national road was analyzed. And, we proposed the regression model to predict the safety rating using the multiple regressions analysis. As the result, it is shown that the evaluation items of slope angle, rainfall and groundwater have a low correlation with safety rating. Also, the regression model suggested by multiple regression analysis shows high predictive value, and it would be possible to apply if the evaluation items of excavation condition and groundwater (rainfall) are not clear.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.557-567
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• 2018

This paper studies the relationship between general investors' allotment ratio and underpricing for the companies that were newly listed in KOSDAQ market after the 20% rule, from March 2004 to December 2013, by empirical analysis. It is shown that the excess allotment ratio over 20% has a strong explanatory power for underpricing ratio under the 1% significance level. Furthermore, the general investors' allotment ratio is a significant explanatory variable of underpricing ratio under the 5% significance level. There are many hypotheses about underpricing, however, if underpricing is evident with high allocation ratio for general investors, it can be regarded as a signal of company's confidence in earnings after listing. In conclusion, this study reveals that general investors' allotment ratio can be used as a major explanatory variable that has a significant effect on the degree of undervaluation in the IPO market.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.231-251
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• 2010

The numbers of SCI paper or patent in science and technology are expected to be related with the number of researcher and knowledge stock (R&D stock, paper stock, patent stock). The results of the regression model showed that severe multicollinearity existed and errors were made in the estimation and testing of regression coefficients. To solve the problem of multicollinearity and estimate the effect of the independent variable properly, principal component regression model were applied for three cases with S&T knowledge production. The estimated principal component regression function was transformed into original independent variables to interpret properly its effect. The analysis indicated that the principal component regression model was useful to estimate the effect of the highly correlate production factors and showed that the number of researcher, R&D stock, paper or patent stock had all positive effect on the production of paper or patent.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1448-1452
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• 2010

최근 기상변동성 증가 및 기후변화 영향으로 수문순환과정이 과거와는 다른 양상으로 전개되고 있으며 전반적으로 극치사상의 빈도 및 강도의 증가현상이 지배적이다. 이러한 영향을 정량적으로 검토하기 위해서 경향성분석 방법 등이 도입되어 극치수문사상의 변동경향을 평가하는데 이용되고 있다. 대표적인 방법으로 선형회귀분석, Mann-Kendall 경향성 분석 등이 있으나 기본적인 가정(assumption)의 제약으로 극치수문자료 계열의 특성을 효과적으로 분석하는데 무리가 있다. 대표적이고 일반적으로 적용되는 선형회귀분석의 경우 자료가 정규분포(normal distribution)의 특성을 가질 때 유효한 방법으로서 극치수문자료와 같이 Heavy Tail를 가지는 분포특성을 표현하는 데는 무리가 따른다. 이밖에도 기존 선형회귀분석을 극치수문자료에 적용할 경우 추정된 결과를 수자원설계의 관심사항인 빈도해석 등에 직접적으로 연계시켜 해석할 수 없는 단점이 있다. 이는 자료계열의 분포특성을 정규분포로 가정하기 때문에 발생하는 문제로서 극치수문자료계열의 분포 특성을 반영할 수 있는 방법론의 개발이 필요하다. 본 연구에서는 이러한 점을 개선하기 위해서 극치분포(extreme distribution)를 선형회귀분석에 적용하는 비정상성빈도해석(nonstationary frequency analysis) 방법론의 개념을 제시하고자 한다. 비정상성빈도해석을 위해서 Bayesian 기법이 도입되며 Bayesian 기법의 특성상 관련변수들이 사후분포(posterior distribution)로 귀결되기 때문에 경향성에 대한 정량적이고 확률적인 분석이 가능한 장점이 있다. 본 연구를 통해 개발된 방법론은 국내외 주요 강수지점에 대해서 적용되며 경향성, 분포특성, 빈도별 강수량에 대한 체계적인 분석이 이루어진다.