• Journal of the Korean Statistical Society
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• v.8 no.1
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• pp.23-36
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• 1979

In the multiple linear regression model a class of weighted least absolute error estimaters, which minimize the sum of weighted absolute residuals, is proposed. It is shown that the weighted least absolute error estimators with Wilcoxon scores are equivalent to the Koul's Wilcoxon type estimator. Therefore, the asymptotic efficiency of the proposed estimator with Wilcoxon scores relative to the least squares estimator is the same as the Pitman efficiency of the Wilcoxon test relative to the Student's t-test. To find the estimates the iterative weighted least squares method suggested by Schlossmacher is applicable.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.597-602
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• 2004

This study examined the forecasting of instant messinger's users choice using neural network. We used the statistical methods which were Logistic Regression, MDA(Multiple Discriminant Analysis), and ANN(Artificial Neural Network). In the result, the forecasting performance of the ANN was better than conventional model(Logistic Regression, MDA).

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.217-223
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• 2016

Outcome misclassification is widespread in classification problems, but methods to account for it are rarely used. In this paper, the problem of inference with misclassified multinomial logit data with a large number of multinomial parameters is addressed. We have had a significant swell of interest in the development of novel methods to infer misclassified data. One simulation study is shown regarding how seriously misclassification issue occurs if the number of categories increase. Then, using the group lasso regression, we will show how the best model should be fitted for that kind of multinomial regression problems comprehensively.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.151-163
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• 2018

The objective of this study was to estimate LOADEST (LOAD Estimator) coefficients for simulating pollutant loads in ungauged watersheds. Regression models of LOADEST were used to simulate pollutant loads, and the multiple linear regression (MLR) was used for coefficients estimation on watershed characteristics. The fifth and third model of LOADEST were selected to simulate T-N (Total-Nitrogen) and T-P (Total-Phosphorous) loads, respectively. The results and statistics indicated that regression models based on LOADEST simulated pollutant loads reasonably and model coefficients were reliable. However, the results also indicated that LOADEST underestimated pollutant loads and had a bias. For this reason, simulated loads were corrected the bias by a quantile mapping method in this study. Corrected loads indicated that the bias correction was effective. Using multiple regression analysis, a coefficient estimation methods according to the watershed characteristic were developed. Coefficients which calculated by MLR were used in models. The simulated result and statistics indicated that MLR estimated the model coefficients reasonably. Regression models developed in this study would help simulate pollutant loads for ungauged watersheds and be a screen model for policy decision.

• Journal of information and communication convergence engineering
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• v.9 no.4
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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.

• Horticultural Science & Technology
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• v.29 no.6
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• pp.549-554
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• 2011

The effect of full bloom date and growing season weather on harvesting date of 'Niitaka' pear (Pyrus pyrifolia) in Naju province and the model of multiple linear regression for predicting the fruit growing days was studied. Earlier year in full bloom date, the harvesting date tended earlier but fruit growing days tended longer. Mean and coefficient of variation of fruit growing degree days (GDD) accumulated daily mean and maximum temperature at the base of $0^{\circ}C$ from full bloom date to harvesting date was 3,565, 2.9% and 4,463, 2.5%, respectively. Fruit growing days was not correlated with the fruit GDD accumulated daily mean and maximum temperature at the base of $0^{\circ}C$ in each month but highly correlated with GDD accumulated daily meteorological factors at days after full bloom date. Especially, it was highly negatively correlated with GDD accumulated daily mean and maximum temperature at the base of $0^{\circ}C$ from $1^{st}$ day after full bloom to $60^{th}$ day. The determination coefficient ($r^2$) of multiple linear regression model by full bloom date, GDD accumulated daily mean and maximum temperature from $1^{st}$ day after full bloom to $60^{th}$ day for predicting fruit growing days was 0.7212. As a result, the fruit growing days of 'Niitaka' pear in Naju province can predict with 72% accuracy by the model of multiple linear regression.

• Korean Journal of Family Social Work
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• no.55
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• pp.31-58
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• 2017

This study aims to explore the longitudinal reciprocal relationship between family conflict and depression level of household head. Using the Korean Welfare Panel study (KOWEPS) of 2006-2009, the study applied multiple regression analysis and autoregressive cross-lagged model to test the hypothesis. Results of multiple regression analysis indicate that single direction of the impact of family conflict on a head of household's levels of depression and the vise versa were statistically significant. That is, higher level of family conflict in 2006 caused an increased levels of depression of household head in 2009, controlling gender, age and depression level in 2006. Also, the higher level of depression of household head in 2006 increased the level of family conflict in 2009 fixed with same control variables. The autoregressive and cross-lagged coefficients of family conflict and a head of household's levels of depression were statistically significant during the 4 years. The findings support the family system theory, indicating that there are reciprocal causal relationships between the whole family conflict and individual depression level. The strategies of social welfare practice and policy should thus aim to decrease individual's levels of depression and improve positive family function simultaneously to break the vicious circle.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.589-605
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• 2023

This paper investigates the ductility demands of steel frames equipped with self-centring fuses under near-fault earthquake motions considering multiple yielding stages. The study is commenced by verifying a trilinear self-centring hysteretic model accounting for multiple yielding stages of steel frames equipped with self-centring fuses. Then, the seismic response of single-degree-of-freedom (SDOF) systems following the validated trilinear self-centring hysteretic law is examined by a parametric study using a near-fault earthquake ground motion database composed of 200 earthquake records as input excitations. Based on a statistical investigation of more than fifty-two (52) million inelastic spectral analyses, the effect of the post-yield stiffness ratios, energy dissipation coefficient and yielding displacement ratio on the mean ductility demand of the system is examined in detail. The analysis results indicate that the increase of post-yield stiffness ratios, energy dissipation coefficient and yielding displacement ratio reduces the ductility demands of the self-centring oscillators responding in multiple yielding stages. A set of empirical expressions for quantifying the ductility demands of trilinear self-centring hysteretic oscillators are developed using nonlinear regression analysis of the analysis result database. The proposed regression model may offer a practical tool for designers to estimate the ductility demand of a low-to-medium rise self-centring steel frame equipped with self-centring fuses progressing in the ultimate stage under near-fault earthquake motions in design and evaluation.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.567-573
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• 2011

This study develops a predictive model for household water end-uses based on data that have measured household characteristics, housing characteristics and other items, surveyed over 3 years in Korea. However, the measured data was left-skewed and it was not fitted to normal distribution. The parameter estimate were biased when using a multiple regression model. In addition, the results of the testing for the model were usually of significance due to the tiny residual from a large number of observations. In order to solve the problem, we suggested log-normal regression model and Weibull regression model as alternatives. The results of this study can be utilized in the planning stages of water and waste water facilities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.961-966
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• 2012

The concern about vacuum glass is enhanced as society gets greener and becomes more concerned about energy savings due to the rising cost of oil. The glass edge sealing process needs the high reliability among the main process for the vacuum glass development in order to maintain between the two glass by the vacuum. In this paper, the process of the edge sealing was performed by using the hydrogen mixture gas which is the high density heat source unlike the traditional method glass edge sealing by using the frit as the soldering process. The ambient temperature in the electric furnace was set in the edge sealing to prevents the thermal impact and transformation of the glasses and the temperature distribution uniformity was measured. The parameter of the edge sealing was set through the basic test and the mathematical relation with the area of the glass edge parts according to the parameter was drawn using the multiple regression analysis method.