• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.431-443
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• 2004

For the purpose of disease mapping, we consider the four small area estimation techniques to estimate the mortality rate of small areas; direct, Empirical estimation with total moment estimator and local moment estimator, Estimation based on hierarchial generalized linear model. The estimators are compared by empirical study based on lung cancer mortality data from 2000 Annual Reports on the Cause of Death Statistics in Gyeongsang-Do and Jeonla-Do published by Korean National Statistical Office. Also he stability and efficiency of these estimators are investigated in terms of mean square deviation as well as variation of estimates.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.627-644
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• 2009

In the knowledge-based economy highlighting the importance of human capital, there has been a growing interest in human capital externalities as a fundamental engine of growth and development of a region. The purpose of this study is to analyze human capital externalities using 3-level hierarchical linear model(3-HLM), decomposing determinants of wages into three levels involving workers(level-1) nested within firms(level-2) nested within regions(level-3). This study separately estimates the effect of the average education level on the wages by three different schooling groups on the assumption that the intensity of knowledge spillovers varies with each group's schooling level. The main results are as follows; First, the coefficient of the average education level of a region shows 0.044, indicating that one-year increase in the average level of schooling could increase average individual earnings by 4.4%. Secondly, the external effects of human capital on three different schooling groups are considerably different, raising less than high school graduates' wages by 3.0%, college graduates' wages by 4.7%, and graduate schools' wages by 11.8%, respectively. Thirdly, well educated workers are much more sensitive to the variation of the regional education level than less educated ones when we apply the shares of each schooling group as alternative measures for the average level of education. Such findings of this study draw an implication that local governments could speed up regional economic growth in the knowledge-based economy by not only raising total human capital stock in a region but building the close networks that promote productivity-enhancing human capital external effects.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.1-20
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• 2024

Regarding the discourse on the correlation between governmental financial support and firm performance, much emphasis has been placed on the role of individual corporate characteristics as well as spatial features. However, there is a notable scarcity of empirical research examining the integrated impact of corporate and cluster characteristics on managerial performance. This study addresses this gap by empirically analyzing the financial and non-financial outcomes resulting from specific allocations of governmental financial support. Additionally, it explores corporate and cluster characteristics predicted to moderate the influence between governmental financial support and firm performance. The analysis employs a two-level hierarchical linear model (HLM) at individual and group levels. The data, reorganized based on business registration numbers at the firm and cluster levels, ultimately utilized panel data from 83,395 firms and 641 clusters. The research findings indicate that governmental financial support demonstrates a positive effect (+) on both sales and patents for firms, suggesting its effectiveness in complementing market failures. Results from the hierarchical linear model analysis show that when combined with human capital capacity, absorptive capacity, and cluster network density, governmental financial support exhibits significant positive effects on sales. This study contributes theoretical and practical insights by analyzing the relationship between governmental financial support and firm performance using a two-level hierarchical linear model. It highlights the role of corporate characteristics such as human capital and absorptive capacity, along with cluster characteristics like cluster network density, in moderating the effects of governmental financial support on firm performance.

• Genomics & Informatics
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• v.20 no.3
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.29-37
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• 2017

It has gradually become important to estimate a forest stand volume utilizing LiDAR data. Recently, various statistical models including a linear regression model has been introduced to estimate a forest stand volume using LiDAR data. One of limitations of the current approaches is in that the accuracy of observed forest stand volume data, which is used as a response variable, is questionable unstable. To overcome this limitation, we consider a spatial structure for a forest stand volume. In this research, we propose a hierarchical model for applying a spatial structure to a forest stand volume. The proposed model is applied to the LiDAR data and the forest stand volume for Bonghwa, Gyeongsangbuk-do.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.662-675
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• 2021

In order to evaluate the effectiveness of the Jeollabuk-do social welfare errand center, this study focused on identifying the effects of users' personal characteristics and workers' job environment characteristics on user satisfaction. The data analyzed are survey data collected from users and workers at 11 social welfare errand centers in each city and county in Jeollabuk-do. In the detailed analysis, the first-level independent variable for the users' personal characteristics, the second-level independent variable for the worker's job environment characteristics, and the user satisfaction were set as the dependent variables, and then a hierarchical linear model analysis model was applied. As a result of the analysis, it was confirmed that households with disabilities, sense of job value, job stress, role overload, and salary satisfaction had a significant effect on the service satisfaction of users. The results of this analysis show that the characteristics of the job environment of the workers have a major effect on the satisfaction of service users. This suggests that there is a strong need for improvement in the fields.

• Journal of the Society of Disaster Information
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• v.14 no.2
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• pp.182-193
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• 2018

Purpose: The purpose of the study is to analyze the relationship between travel behavior and urban spatial characteristics in a hierarchical manner. Method: This study analyzed the relationship between traffic patterns and urban spatial characteristics for 83 cities in Korea by using a hierarchical linear model. Results: It was found that the urban spatial characteristics influenced the choice of transportation mode and travel time with personal attributes. However, the degree of influence on the choice of the means and the time required is relatively low through the policy of changing the city attribute, so the policy effect of mobilizing the land use policy for the traffic is theoretically, but the scale is not bigger than expected. Conclusion: In high density or the bigger scale of the city, the mass transportation system is widely supplied and used, but it does not overcome the drawback that it takes more time than the autos.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.759-771
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• 2005

Twin studies are one of the most widely used methods for quantifying the influence of genetic and environmental factors on some traits such as a life span or a disease. In this paper we propose a genetic mixed linear model for twin survival time data, which allows us to separate the genetic component from the environmental component. Inferences are based upon the hierarchical likelihood (h-likelihood), which provides a statistically efficient and simple unified framework for various random-effect models. We also propose a simple and fast computation method for analyzing a large data set on twin survival study. The new method is illustrated to the survival data in Swedish Twin Registry. A simulation study is carried out to evaluate the performance.

• 대한예방의학회:학술대회논문집
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• 2002.10a
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• pp.341-342
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• 2002

• 대한예방의학회:학술대회논문집
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• 2002.10a
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• pp.197-198
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• 2002