• Earthquakes and Structures
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• v.22 no.6
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• pp.609-623
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• 2022

To study the empirical seismic fragility of a reinforced concrete girder bridge, based on the theory of numerical analysis and probability modelling, a regression fragility method of a rapid fragility prediction model (Gaussian first-order regression probability model) considering empirical seismic damage is proposed. A total of 1,069 reinforced concrete girder bridges of 22 highways were used to verify the model, and the vulnerability function, plane, surface and curve model of reinforced concrete girder bridges (simple supported girder bridges and continuous girder bridges) considering the number of samples in multiple intensity regions were established. The new empirical seismic damage probability matrix and curve models of observation frequency and damage exceeding probability are developed in multiple intensity regions. A comparative vulnerability analysis between simple supported girder bridges and continuous girder bridges is provided. Depending on the theory of the regional mean seismic damage index matrix model, the empirical seismic damage prediction probability matrix is embedded in the multidimensional mean seismic damage index matrix model, and the regional rapid prediction matrix and curve of reinforced concrete girder bridges, simple supported girder bridges and continuous girder bridges in multiple intensity regions based on mean seismic damage index parameters are developed. The established multidimensional group bridge vulnerability model can be used to quantify and predict the fragility of bridges in multiple intensity regions and the fragility assessment of regional group reinforced concrete girder bridges in the future.

• Korean System Dynamics Review
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• v.8 no.2
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• pp.25-49
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• 2007

An important assumption of the classical linear regression model is that the disturbances appearing in the population regression function are homoskedastic; that is, they all have the same variance. If we persist in using the usual testing procedures despite heteroskedasticity, what ever conclusions we draw or inferences we make be very misleading. The contribution of this paper will be to the concrete procedure of the proper estimation when the heteroskedasticity does exist in the data, because the quality of dependent variable predictions, i.e., the estimated variance of the dependent variable, can be improved by giving consideration to the issues of regional homogeneity and/or heteroskedasticity across the research area. With respect to estimation, specific attention should be paid to the selection of the appropriate strategy in terms of the auxiliary regression model. The paper shows that by testing for heteroskedasticity, and by using robust methods in the presence of with and without heteroskedasticity, more efficient statistical inferences are provided.

• The Korean Journal of Health Service Management
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• v.8 no.2
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• pp.11-22
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• 2014

This study purposed to analyze the regional variation of the local-out rates of inpatient services. Multiple data sources collected from National Health Insurance Corporation and statistics Korea were merged to produce the analysis data set. The unit of analysis in this study was city, Gun, Gu, and all of them were included in analysis. The dependent variable measured the local-out rate of inpatient cost in study regions. Local environments were measured by variables in three dimensions: provider factors, socio-demographic factors, and health status. Along with the traditional ordinary least square (OLS) based regression model, geographically weighted regression (GWR) model were applied to test their effects. SPSS v21 and ArcMap v10.2 were applied for the statistical analysis. Results from OLS regression showed that most variables had significant relationships with the local-out rate of inpatient services. However, some variables had shown diverse directions in regression coefficients depending on regions in GWR. This implied that the study variables might not have consistent effects and they may varied depending the locations.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.75-98
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• 2021

This paper compares three statistical models that examine the relationship between national and provincespecific fertility rates. The three models are two of the regression models and a cointegration model. The regression model is by substituting Gompit transformation for the cumulative fertility rate by the average for ten years, and this model applies the raw data without transformation of the fertility data. A cointegration model can be considered when fitting the unstable time series of fertility rate in probability process. This paper proposes the following when it is intended to derive the relation of non-stationary fertility rate between the national and provinces. The cointegrated relationship between national and regional fertility rates is first derived. Furthermore, if this relationship is not significant, it is proposed to look at the national and regional fertility rate relationships with a regression model approach using raw data without transformation. Also, the regression model method of substituting Gompit transformation data resulted in an overestimation of fertility rates compared to other methods. Finally, Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon and Gyeonggi province are expected to show a total fertility rate of 1.0 or less from 2025 to 2030, so an urgent and efficient policy to raise this level is needed.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.677-686
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• 2018

There remain numerous ungauged watersheds in Korea owing to limited spatial and temporal streamflow data with which to estimate hydrological model parameters. To deal with this problem, various regionalization approaches have been proposed over the last several decades. However, the results of the regionalization models differ according to climatic conditions and regional physical characteristics, and the results of the regionalization models in previous studies are generally inconclusive. Thus, to improve the performance of the regionalization methods, this study attaches hydrological model parameters obtained using a spatial proximity model to the explanatory variables of a regional regression model and defines it as a hybrid regionalization model (hybrid model). The performance results of the hybrid model are compared with those of existing methods for 37 test watersheds in South Korea. The GR4J model parameters in the gauged watersheds are estimated using a shuffled complex evolution algorithm. The variation inflation factor is used to consider the multicollinearity of watershed characteristics, and then stepwise regression is performed to select the optimum explanatory variables for the regression model. Analysis of the results reveals that the highest modeling accuracy is achieved using the hybrid model on RMSE overall the test watersheds. Consequently, it can be concluded that the hybrid model can be used as an alternative approach for modeling ungauged watersheds.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1147-1154
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• 2016

Impact of Korea train express (KTX) on the regional economy in Korea has been studied by many researchers. Current research is limited in the lack of quantitative research using a statistical model to study the effect of KTX on regional economy. This paper analyses the influence of KTX to the household final consumption expenditure, which is one of important regional economic index, using spatial panel regression model. The spatial structure is introduced through spatial autocorrelation matrix using adjacency of KTX connection. The result shows a significant effect of Korea train express on the regional economy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.741-750
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• 2021

The research aims to analyze the level of efficiency by grouping banks during the period 2017 - 2018 into category 1 and category 2 banks and then dividing them as Regional Development Banks (BPD) and Non-BPD Conventional Commercial Banks (BUK) within each category. The research objects are banks within the categories BPD and BUK comprised 18 BPDs and 35 BUKs. The research methodology uses 3 stages, first, using Data Envelopment Analysis (DEA) we measure the level of bank efficiency; second, using the Tobit regression model we evaluate the effect of financial performance on DEA efficiency, and third, using the Mann-Whitney test we determine whether there is a difference in the efficiency of category 1 and 2 banks. The results showed that there was a decrease in the efficiency of category 1 and 2 banks but on average, the efficiency of category 1 banks is higher than category 2 banks. The estimation results of the Tobit regression model show that only the ROA variable affects the efficiency level of category 1 banks, while category 2 banks are influenced by NPL and ROA variables. In the Mann-Whitney test, it was proven that there were differences in efficiency between BUK and BPD in category 1 and 2 banks.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.71-80
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• 2009

Although desire for living without hazardous damages grows these days, threats from natural disasters which we are currently exposed to are quiet different from what we have experienced. To cope with this changing situation, it is necessary to assess the characteristics of the natural disasters. Therefore, the main purpose of this research is to suggest a methodology to estimate the potential property loss and assess the flood risk using a regional regression analysis. Since the flood damage mainly consists of loss of lives and property damages, it is reasonable to express the results of a flood risk assessment with the loss of lives and the property damages that are vulnerable to flood. The regional regression analysis has been commonly used to find relationships between regional characteristics of a watershed and parameters of rainfall-runoff models or probability distribution models. In our research, however, this model is applied to estimate the potential flood damage as follows; 1) a nonlinear model between the flood damage and the hourly rainfall is found in gauged regions which have sufficient damage and rainfall data, and 2) a regression model is developed from the relationship between the coefficients of the nonlinear models and socio-economic indicators in the gauged regions. This method enables us to quantitatively analyze the impact of the regional indicators on the flood damage and to estimate the damage through the application of the regional regression model to ungauged regions which do not have sufficient data. Moreover the flood risk map is developed by Flood Vulnerability Index (FVI) which is equal to the ratio of the estimated flood damage to the total regional property. Comparing the results of this research with Potential Flood Damage (PFD) reported in the Long-term Korea National Water Resources Plan, the exports' mistaken opinions could affect the weighting procedure of PFD, but the proposed approach based on the regional regression would overcome the drawback of PFD. It was found that FVI is highly correlated with the past damage, while PFD does not reflect the regional vulnerabilities.

• Health Policy and Management
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• v.30 no.1
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• pp.26-36
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• 2020

Background: The purpose of this study was to analyze the relationship between the regional characteristics and the age-adjusted cardio-cerebrovascular disease mortality rates (SCDMR) in 229 si·gun·gu administrative regions. Methods: SCDMR of man and woman was used as a dependent variable using the statistical data of death cause in 2017. As a representative index of regional characteristics, health behavior factors, socio-demographic and economic factors, physical environment factors, and health care factors were selected as independent variables. Ordinary least square (OLS) regression and geographically weighted regression (GWR) were performed to identify their relationship. Results: OLS analysis showed significant factors affecting the mortality rates of cardio-cerebrovascular disease as follows: high-risk drinking rates, the ratio of elderly living alone, financial independence, and walking practice rates. GWR analysis showed that the regression coefficients were varied by regions and the influence directions of the independent variables on the dependent variable were mixed. GWR showed higher adjusted R² and Akaike information criterion values than those of OLS. Conclusion: If there is a spatial heterogeneity problem as Korea, it is appropriate to use the GWR model to estimate the influence of regional characteristics. Therefore, results using the GWR model suggest that it needs to establish customized health policies and projects for each region considering the socio-economic characteristics of each region.

• Journal of agricultural medicine and community health
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• v.41 no.4
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• pp.183-194
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• 2016

Objectives: The purpose of this study was to investigate the differences of capacity of local health organization to regional characteristics and the influence of organizational capacity on organizational performance. Methods: The study used the secondary data for 160 local public health organizations from $5^{th}$ Community Health Plans and 2009 Community Health Survey. The collected data were analyzed using one-way ANOVA and multiple regression analysis. Results: Work force and budget showed differences in regional size and elderly population rate. And consumer satisfaction and health care utilization showed differenced in work force and budget. The regression model with total number of employee, number of registered nurses, number of doctors and budget against consumer satisfaction was statistically significant (F=14.70, p=<.001), and number of registered nurses was identified as a factor influencing consumer satisfaction. This model also explained 20.5% of service satisfaction. The regression model for consumer satisfaction was statistically significant (F=45.98, p=<.001), and total number of employee nurses was identified as a factor influencing health care utilization. This model also explained 53.1% of utilization. Conclusions: The findings of this study imply that organizational capacity as work force and budget should be increased to improve the organizational performance as consumer satisfaction and health care utilization.