• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.371-381
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• 2000

In this paper, we describes smoothing spline in nonparametric regression and some asymptotic results for estimates of the regression cofficients in the parametric part were biased on semi parametric regression estimator.

• Journal of Environmental Impact Assessment
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• v.4 no.2
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• pp.93-103
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• 1995

This study was carried out to suggest the best statistical test to analyze the trend in monthly water quality data. Traditional parametric tests such as t-test and regression analysis are based on the assumption that the underlying population has a normal distribution and regression analysis additionally assumes that residual errors are independent. Analyzing 9-years monthly COD data collected at Paldang in Han River, the underlying population was found to be neither normal nor independent. Therefore parametric tests are invalid for trend detection. Four Kinds of nonparametric statistical tests, such as Run Test, Daniel test, Mann-Kendall test, and Time Series Residual Analysis were applied to analyze the trend in the COD data, Daniel test and Mann-Kendall test indicated upward trend in COD data. The best nonparametric test was suggested to be Daniel test, which is simple in computation and easy to understand the intuitive meaning.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.515-524
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• 2007

This paper deals with local linear estimation of fuzzy regression models based on Diamond(1998) as a new class of non-linear fuzzy regression. The purpose of this paper is to introduce a use of smoothing in testing for lack of fit of parametric fuzzy regression models.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.922-925
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• 2008

Many highway projects are under way in Korea. However, owners frequently find that the project cost exceeds the budget and they are unable to identify the underlining reasons. The main purpose of this research is to develop cost models for transportation projects in Korea using the multiple linear regression (MLR). The data consist of 27 completed transportation projects, built from 1991 to 2001, The technique of multiple regression analysis is used to develop the parametric cost estimating model for total budget cost per highway square meter (TBC/$m^2$). Findings of the study indicated that MLR car be applied to highway projects in Korea. There are twf) major contributions of this research. (1) the identification of transportation parameters as a significant cost driver for transportation costs and (2) the successful development of the parametric cost estimating models for transportation projects in Korea.

• Geomechanics and Engineering
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• v.14 no.4
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• pp.315-324
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• 2018

With rapid economic growth, numerous deep excavation projects for high-rise buildings and subway transportation networks have been constructed in the past two decades. Deep excavations particularly in thick deposits of soft clay may cause excessive ground movements and thus result in potential damage to adjacent buildings and supporting utilities. Extensive plane strain finite element analyses considering small strain effect have been carried out to examine the wall deflections for excavations in soft clay deposits supported by diaphragm walls and bracings. The excavation geometrical parameters, soil strength and stiffness properties, soil unit weight, the strut stiffness and wall stiffness were varied to study the wall deflection behaviour. Based on these results, a multivariate adaptive regression splines model was developed for estimating the maximum wall deflection. Parametric analyses were also performed to investigate the influence of the various design variables on wall deflections.

• Structural Engineering and Mechanics
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• v.55 no.1
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• pp.1-27
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• 2015

This article presents a theoretical parametric analysis on the ultimate torsional behaviour of axially restrained reinforced concrete (RC) beams. This analysis is performed by using a computing procedure based on a modification of the Variable Angle Truss Model. This computing procedure was previously developed to account for the influence of the longitudinal compressive stress state due to the axial restraint conditions provided by the connections of the beams to other structural members. The presented parametric study aims to check the influence of some important variable studies, namely: torsional reinforcement ratio, compressive concrete strength and axial restraint level. From the results of this parametric study, nonlinear regression analyses are performed and some design charts are proposed. Such charts allow to correct the resistance torque of RC beams (rectangular sections with small height to width ratios) to account for the favorable influence of the axial restraint.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.207-211
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• 2008

The importance of cost management in early stage has been increasing due to market change and competition severence in construction industry. Because the adjustable budget is only 20% after finishing design stage, the critical decision is made in the early stage. However, in the early stage, the design information is not enough to make crucial decision. Therefore, this research suggests the predicting method on the purpose of accurate cost estimation. The parametric estimation is appropriate for the early stage, especially it has the strength of rapidity in cost estimation. This research analyzes 84 actual data of public apartment on the scale of $11{\sim}15$ stories, and then performs the correlation analysis between cost and influence factors. After eliminating the parameters which causes the problem of multicollinearity, this research derived the formula through the multi-regression analysis.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2006.11b
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• pp.411-416
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• 2006

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.