• 한국지반환경공학회 논문집
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• 제9권4호
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• pp.37-45
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• 2008

우리나라 서, 남해안은 정규압밀 또는 약간 과압밀된 연약 점성토층이 널리 분포하고 있다. 이러한 연약지반의 효율적이고 경제적인 설계와 시공을 위해서는 사전에 공학적 특성을 상세히 파악하는 것이 중요하다. 본 연구에서는 한반도 남해안 해성점토에 대하여 자연함수비, 비중, 전체단위중량, 초기간극비, 액성한계, 소성한계, 활성도의 물리적 특성을 파악하고 토질정수간의 상관성을 규명하였다. 분석을 위하여 비교적 신뢰성이 크다고 볼 수 있는 대형 항만공사용 최근자료를 수집하여 이용하였다. 상관관계분석에서 선형회귀분석과 비선형회귀분석을 통하여 최적의 값을 도출하였다. 본 분석에 사용된 통계 소프트웨어는 SPSS(Version10.0)을 이용하였다. 분석결과 토질정수 사이의 선형및 비선형 회귀분석결과 함수비와 초기간극비의 상관성이 가장 큰 것으로 나타났으며 선형 및 누승형 회귀분석에서 동일한 결정계수를 나타내 주고 있다. 기타 다른 정수사이의 상관성은 누승식 및 지수승식 형태의 비선형 회귀분석이 선형회귀분석보다 양호한 상관성을 보여주고 있다.

• 대한조선학회논문집
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• 제51권6호
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.

• 대한수학회논문집
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• 제33권3호
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• pp.973-983
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• 2018

Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• 응용통계연구
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• 제22권4호
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• pp.865-877
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• 2009

본 연구에서는 서울지역 오존의 기상상태와 추세경향에 따른 고농도 현상을 모수적 방법인 비선형회귀모형(nonlinear regression model)으로 모형화 하였다. 여기서는 1995년부터 1999년까지의 자료로부터 오존과 고농도 현상에 영향을 줄 수 있는 기상상태와 추세경향 등을 순차적으로 추가함으로써 고농도 현상을 예측하는 모형을 추정하였다.

• 한국농공학회논문집
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• 제56권4호
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• pp.29-39
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• 2014

In this study, we performed a non-stationary frequency analysis using a power model and the model was applied for Seoul, Daegu, Daejeon, Mokpo sites in Korea to estimate the probable precipitation amount at the target years (2020, 2050, 2080). We used the annual maximum precipitation of 24 hours duration of precipitation using data from 1973 to 2009. We compared results to that of non-stationary analyses using the linear and logistic regression. The probable precipitation amounts using linear regression showed very large increase in the long term projection, while the logistic regression resulted in similar amounts for different target years because the logistic function converges before 2020. But the probable precipitation amount for the target years using a power model showed reasonable results suggesting that power model be able to reflect the increase of hydrologic extremes reasonably well.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.303-312
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• 1995

We consider the following type of general semi-parametric non-linear regression model : $y_i = f_i(\theta) + \epsilon_i, i=1, \cdots, n$ where ${f_i(\cdot)}$ represents the set of non-linear functions of the unknown parameter vector $\theta' = (\theta_1, \cdots, \theta_p)$ and ${\epsilon_i}$ represents the set of measurement errors with unknown distribution. Under suitable finite-sample, small-dispersion asymptotic framework, we derive a general lower bound for the asymptotic mean squared error (AMSE) matrix of the Gauss-consistent estimator of $\theta$. We then prove the fundamental result that the general non-linear least squares estimator (NLSE) is an optimal estimator within the class of all regular Gauss-consistent estimators irrespective of the type of the distribution of the measurement errors.

• 한국지반공학회논문집
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• 제19권6호
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• pp.371-378
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• 2003

한반도 서, 남해안 해성점토에 대하여 초기간극비와 자연함수비 및 액성한계의 물리적 특성과 압축지수와의 상관성을 규명하기 위해 비교적 신뢰성이 크다고 볼 수 있는 대형 항만 공사용 최근자료를 분석하였다. 시료교란의 정도를 분석하기 위하여 각 표본별 실내압밀시험을 실시하고 Schmertmann이 제안한 방법으로 보정하여 수정압축지수를 산정하였다. 또한 이들 자료를 토대로 실내압밀시험으로부터 얻어진 압축지수를 경험적 방법에 의하여 보정한 후 현장 처녀압축곡선과의 관계를 분석하고, 단순회귀분석, 다중회귀분석 및 비선형 회귀분석을 실시하여 최적의 회귀모델을 구한 후 해성점토에 적용할 수 있는 토질특성과 시료교란의 영향을 고려한 압축지수와의 상관 관계식을 제안하였다. 분석 결과, 시료교란의 영향을 경험적 방법으로 평가해 본 결과 현장 압축지수는 실험실 압축지수의 1.16배정도 크게 평가되었다. 해성점토의 물리적 특성과 압축지수의 상관성에 대한 최적의 회귀모형은 토질정수의 누승식 또는 지수승식 형태의 비선형회귀식이 가장 적합한 것으로 나타났다. 또한, 설계 및 실무에 보다 쉽게 적용할 수 있도록 하기 위하여 선형관계식을 사용하는 경우에는 압축지수의 상관식을 물성치의 구간에 따라 구분하여 사용하는 것이 바람직하다.

• Communications for Statistical Applications and Methods
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• 제20권2호
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• pp.147-155
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• 2013

To determine whether each coefficient is equal to zero or not, usual $t$-tests are a popular choice (among others) in linear regression to practitioners because all statistical packages provide the statistics and their corresponding $p$-values. Under smaller samples (especially with non-normal errors) the tests often fail to correctly detect statistical significance. We propose a permutation approach by adopting a sufficient dimension reduction methodology to overcome this deficit. Numerical studies confirm that the proposed method has potential advantages over the t-tests. In addition, data analysis is also presented.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.371-374
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• 2008

In very high-spatial resolution remote sensing imagery, it is difficult to extract the feature information of various objects because of occlusion and shadows. Moreover, various and feeble information within shadows can be of use in GIS-based applications and remote sensing analysis. In this paper, we developed a radiometric restoration method for shadow areas using KOMPSAT-2 satellite image. After detecting the shadow, non-shadow pixels nearby are extracted using a morphological filter. An iterative linear regression method is applied to calculate the relationship between shadow and non-shadow pixels. The shadows are restored by the parameters of the linear regression algorithm. Tests show that recovery of shadowed areas by our method leads to improved image quality.