• 한국대기환경학회지
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• 제26권5호
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• pp.533-542
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• 2010

The $PM_{10}$ concentrations in the underground should be monitored for the health of commuters on the underground subway system. Seoul Metro and Seoul Metropolitan Rapid Transit Corporation are measuring several air pollutants regularly. As for the measurement of $PM_{10}$ concentrations, instruments based on $\beta$-ray absorption method and gravimetric methods are being used. But the instruments using gravimetric method give us 20-hour-average data and the $\beta$-ray instruments can measure the $PM_{10}$ concentration every one hour. In order to keep the $PM_{10}$ concentrations under a healthy condition, the air quality of the underground platform and tunnels should be monitored and controlled continuously. The $PM_{10}$ instruments using light scattering method can measure the $PM_{10}$ concentrations every less than one minute. However, the reliability of the instruments using light scattering method is still not proved. The purpose of this work is to study the reliability of the instruments using light scattering method to measure the $PM_{10}$ concentrations continuously in the underground platforms. One instrument using $\beta$-ray absorption method and two different instruments using light scattering method (LSM1, LSM2) were placed at the platform of the Jegi station of Seoul metro line Number 1 for 10 days. The correlation between the $\beta$-ray instrument and the LSM2 ($r^2$=0.732) was higher than that between the $\beta$-ray instrument and the LSM1 ($r^2$=0.393). Thus the LSM2 was chosen for further analysis. Three different regression analysis methods were tested: Linear regression analysis, Nonlinear regression analysis and Orthogonal regression analysis. When the instruments using light scattering method were used, the data measured these instruments have to be converted to actual $PM_{10}$ concentrations using some factors. With these analyses, the factors could be calculated successfully as linear and nonlinear forms with respect to the data. And the orthogonal regression analysis was performed better than the ordinary least squares method by 28.45% reduction of RMSE. These findings propose that the instruments using light scattering method light scattering method can be used to measure and control the $PM_{10}$ concentrations of the underground subway stations.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.187-191
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• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• 한국정보통신학회논문지
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• 제13권9호
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• pp.1935-1940
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• 2009

WSN(Wireless Sensor Network)의 기술 요소 중의 하나는 센서 노드의 위치 관리이다. GPS, 초음파 센서, RSSI 등이 전형적인 노드의 위치 관리 방법이다. 본 논문에서는 센서 노드 위치 측정의 정확성을 향상시키기 위해 RSSI 측정에 회귀분석을 적용한 새로운 위치 관리 방식을 제안한다. 또한 기존 방식의 실험적인 결과와의 비교를 통해 제안된 방식의 성능을 평가한다. 결과에 따르면 우리 의 제안된 방식인 LM-RAR은 RSSI와 Friis 공식을 사용한 기존의 위치 관리 방식보다 향상된 정확성을 보인다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.547-550
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• 2006

This study is to suggest a methodology to produce landslide hazard map by combining LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) Approach. Topographic factors (slope, aspect, elevation), soil drain, soil depth and land use were adopted to classify landslide hazard areas. The method was applied to a 520 $km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9 % matching rate for the real landslide sites comparing with the classified areas of high-risk landslide while LRA and AHP showed 46.1 % and 48.7 % matching rates respectively.

• 대한의용생체공학회:의공학회지
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• 제22권4호
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• 한국수자원학회논문집
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• 제41권1호
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• pp.17-25
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• 2008

수위-유량관계식의 유도와 실무적용에 있어 통상적으로 회귀분석의 특성을 간과하고 사용하는 경우가 종종 발생한다. 예를 들어 실무에서는 관측수위로부터 관측유량으로 회귀분석되어 만들어진 수위-유량관계식을 홍수모형으로부터 모의된 설계홍수유출량으로부터 설계홍수위를 환산하는데 사용되기도 한다. 그러나 독립과 종속변수가 서로 바뀌면, 관측치와 회귀식간 연직거리의 잔차들로부터 유도된 기존의 회귀분석에 의하여, 회귀식이 서로 달라지기 때문에 역으로 적용하여서는 안 된다. 본 연구에서는 이런 문제점을 해결하기위해 회귀식의 변수들을 상호 교환할 수 있는 최소자승 회귀분석의 새로운 알고리즘을 제안하였다. 새로운 방법을 낙동강유역의 본류 5개 수위표지점의 수위-유량관계식에 대하여 적용하였다. 3가지 회귀식이 유도되었는데, 이들은 각각 수위로부터 유량으로(model 1), 유량으로부터 수위로(model 2) 그리고 양방향(model 3)으로 유도된 수위-유량관계식을 비교하여 실무에서 잘못 적용되는 실수를 줄일 수 있는 새로운 방법을 제시하였다.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.643-648
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• 2012

여러 임상자료를 이용하여 반응변수와 설명변수간의 관계를 규명하는 분석이 많이 이루어지고 있다. 이를 위해서 회귀분석이 흔히 사용되고 있으며, 이를 통해 설명변수가 반응변수를 얼마나 설명하는지 또한 모형이 얼마나 자료에 적합한지에 대해 분석하고 있다. 그러나 임상자료로 분석된 회귀모형에 대한 타당성 확인은 대부분 분석된 회귀모형이 얼마나 자료를 설명하는가를 나타내는 결정계수만을 살펴보는 것에 그치고 있다. 결정계수 이외의 다른 방법으로도 분석된 회귀모형의 회귀계수에 대한 타당성을 확인할 필요가 있다. 따라서 본 논문에서는 잭나이프 회귀분석과 붓스트랩 회귀분석을 이용하여 임상자료로 분석한 회귀모형의 회귀계수에 대한 타당성을 확인하는 방법을 소개하고자 한다.

• Journal of the Korean Data and Information Science Society
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• 제2권
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• pp.1-9
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• 1991

Shin, Tarumi and Tanaka(1989) discussed a method of sensitivity analysis in principal component regression(PCR) based on an influence function derived by Tanaka(1988). The present paper is its continuation. In this paper we first consider two new influence measures, then apply the proposed method to various data sets and discuss some properties of sensitivity analysis in PCR.

• 한국지능시스템학회논문지
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• 제23권4호
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• pp.366-370
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• 2013

설명변수와 반응변수 사이의 통계적 관계를 설명하기 위해 사용되는 회귀모형을 분석하는 방법을 회귀분석이라 한다. 본 논문에서는 독립변수와 종속변수에 대한 퍼지관계를 표현하는 퍼지회귀모형를 추정하기 위하여 이상치에 민감하지 않은 로버스트한 추정량인 Theil방법을 소개한다. Theil방법은 설명변수와 반응변수의 ${\alpha}$-수준집합의 각 성분으로 구성된 집합에서 선택한 임의의 두 쌍 자료로부터 계산된 변화율의 중위수를 두 변수에 대한 변화량의 추정량으로 간주한다. 본 논문에서 제안된 Theil방법이 최소자승법을 이용하여 추정된 퍼지회귀모형보다 더 정확할 수 있음을 예제를 통하여 확인한다.

• Communications for Statistical Applications and Methods
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• 제19권2호
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.