• 자원환경지질
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• 제17권3호
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• pp.163-169
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• 1984

암석의 대표적 열원치(熱源値)를 사용하여 지각 열류량(熱流量)의 반전(反轉)을 연구하였으며 2-D 모델은 아주 얇은 정방형판(正方形板)이 고려되었다. 포텐샬 이론을 기초로 하여 지각 열류량과 열원 사이의 새로운 관계를 도출하였으며 두가지 경우의 계산결과가 도시되어 있다. Random search 방법과 ridge regression방법이 비교되었으며 지각열류량의 반전(反轉) 연구에서는 random search 방법의 중요성이 발견되었다.

• 한국수학사학회지
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• 제11권2호
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• pp.63-76
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• 1998

When teaching the linear regression analysis in the class, the power transformation must be introduced to fit the linear regression model for nonlinear data. Box and Cox (1964) proposed the attractive power transformation technique which is so called Box-Cox transformation. In this paper, an effective algorithm selecting an appropriate value for Box-Cox transformation is developed which is considered to find a value minimizing error sum of squares. When the proposed algorithm is used to find a value for transformation, the number of iterations needs to be considered. Thus, the number of iterations is examined through simulation study. Since SAS is one of most widely used packages and does not provide the procedure that performs iterative Box-Cox transformation, a SAS program automatically choosing the value for transformation is developed. Hence, the students could learn how the Box-Cox transformation works, moreover, researchers can use this for analysis of data.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1998년도 봄 학술발표회 논문집(I)
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• pp.347-352
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• 1998

The purpose of this paper is to present and establish a procedure to predict the prestress forces during the service life of the structure. The statistical approach of this procedure is using the in-situ measurement data of the post-tensioning system to develop a nonlinear regression analysis. The method of least squares is used to fit a certain function a set of data. Use of a nonlinear model is achieved by its logarithmic transformation and sunsequent use of linear-regression theory. The regression analysis result can be used to check the prestress force during the service life so that the remaining prestress force is equal to or exceeds the design requirement. Results from the measurement data of PSC box girder bridge structure were used to demonstrate the procedures.

• 한국태양에너지학회 논문집
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• 제39권6호
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• pp.113-125
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• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.

• 품질경영학회지
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• 제23권4호
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• pp.58-63
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• 1995

To access the quality of a fit to a set of data it is always useful to conduct a posteriori analysis involving the examination of residuals, detection of influential data values, etc. Smoothing splines are a type of nonparametric regression estimators for the diagnostic problem. And leverage value, Cook's distance, and DFFITS are used for detecting influential data. Since high leverage points will always have small residuals, the new diagnostic measures including of properties of leverage and residuals are needed. In this paper, we propose FVARATIO version as diagnostic measure in nonparametric regression. Also we consider the rough bound as analogy with linear regression case.

• Communications for Statistical Applications and Methods
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• 제21권5호
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• pp.423-433
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• 2014

Test statistics using cumulative sums of residuals have been widely used in various regression models including generalized linear models(GLM). Recently, Pan and Lin (2005) extended this testing procedure to the generalized linear mixed models(GLMM) having random effects, in which we encounter difficulties in computing the marginal likelihood that is expressed as an integral of random effects distribution. The Gaussian quadrature algorithm is commonly used to approximate the marginal likelihood. Many commercial statistical packages provide an option to apply this type of goodness-of-fit test in GLMs but available programs are very rare for GLMMs. We suggest a computational algorithm to implement the testing procedure in GLMMs by a freely accessible R package, and also illustrate through practical examples.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.809-817
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• 2012

We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.475-482
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• 2011

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

• 전기학회논문지
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• 제64권12호
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.

• 간호행정학회지
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• 제22권2호
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• pp.158-166
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• 2016

Purpose: This study was done to investigate the influence of organization and community job embeddedness on turnover intention of nurses in small and medium sized general hospitals. Methods: The participants, 333 nurses, were recruited from small and medium sized general hospitals in Korea. Data were collected by self-report questionnaires on job-embeddedness and turnover intention and were analyzed using descriptive statistics, Independent t-test, One-way ANOVA and $Scheff{\acute{e}}^{\prime}$, Pearson correlation coefficient, and multiple linear regression analysis with the SPSS 18.0 program. Results: The score for job embeddedness and it's 6 factors, and turnover intention were above 3 on a 5 point scale with the exception of organizational sacrifice. There was a significant difference in turnover intention according to age, marital status, salary, and position. There were significant negative correlations between the 6 factors of job embeddedness and turnover intention. Variables entered in multiple regression showed that organizational sacrifice, organizational fit and age were significant contributing factors to turnover intention. Conclusion: The findings suggest that there is a need for strategies to enhance job embeddedness, especially organization sacrifice and organization fit. These factors should be developed and used to decrease turnover intention of nurses in small and medium sized general hospitals.