• International Journal of Quality Innovation
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• v.3 no.2
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• pp.174-182
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• 2002

System identification and controller formulation are essential in dynamic process control. In system identification, data for system identification are obtained, and then they are analyzed so that the system model of the process is built, identified, and diagnosed. In controller formulation, the control equation is derived based on the result of the system identification. There has been much theoretical research on system identification and controller formulation. These theories are very useful when they are appropriately applied. To our regret, however, these theories are not always effectively applied in practice because the engineers and the operators who manage the process often do not have the necessary understanding of required time series analysis methods. On the other hand, because of widespread use of statistical packages, system identification such as estimating ARMA models can be done with little understanding of time series analysis methods. Therefore, it might be said that the most theoretically difficult part in practice is the controller formulation. In this paper, lists of control equations are proposed as a useful tool for practitioners to use. The tool supports bridging the gap between theory and practice in dynamic process control. Also, for some models, the generalized control equations are obtained.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.211-218
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• 2014

Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data with time-dependent covariates, when the implicit full covariates conditional mean (FCCM) assumption is violated, the QIF can not provide more consistent and efficient estimator than GEE (Cho and Dashnyam, 2013). Lai and Small (2007) divided time-dependent covariates into three types and proposed generalized method of moment (GMM) for longitudinal data with time-dependent covariates. They showed that their GMM type II and GMM moment selection methods can be more ecient than GEE with independence working correlation (GEE-ind) in the case of type II time-dependent covariates. We develop upgraded QIF method for type II time-dependent covariates. We show that this upgraded QIF method can provide substantial gains in efficiency over QIF and GEE-ind in the case of type II time-dependent covariates.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.651-658
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• 2013

For the marginal model and generalized estimating equations (GEE) method there is important full covariates conditional mean (FCCM) assumption which is pointed out by Pepe and Anderson (1994). With longitudinal data with time-varying stochastic covariates, this assumption may not necessarily hold. If this assumption is violated, the biased estimates of regression coefficients may result. But if a diagonal working correlation matrix is used, irrespective of whether the assumption is violated, the resulting estimates are (nearly) unbiased (Pan et al., 2000).The quadratic inference functions (QIF) method proposed by Qu et al. (2000) is the method based on generalized method of moment (GMM) using GEE. The QIF yields a substantial improvement in efficiency for the estimator of ${\beta}$ when the working correlation is misspecified, and equal efficiency to the GEE when the working correlation is correct (Qu et al., 2000).In this paper, we interest in whether the QIF can improve the results of the GEE method in the case of FCCM is violated. We show that the QIF with exchangeable and AR(1) working correlation matrix cannot be consistent and asymptotically normal in this case. Also it may not be efficient than GEE with independence working correlation. Our simulation studies verify the result.

• Korean Journal of Social Welfare Studies
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• v.48 no.4
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• pp.33-57
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• 2017

As there is a growing concern about the steady increase in the consumer debt and its potential consequences on individuals and families, this study examined the association between personal debt and the risk of domestic violence, which in this study is referred to as violence between man and woman who have a spousal relationship. We used the data from the Korea Welfare Panel Study collected from 2009 to 2016. We applied a generalized estimating equation approach for the analysis of panel data. The results show that the higher the ratio of personal debt to disposable income and the ratio of debt payment to disposal income is, the greater the risk of domestic violence. While the debt to income ratio played a role regarding was related to a heightened risk of domestic violence among the poor group, the debt payment to income ratio was associated with a higher risk of domestic violence among the non-poor group. Implications of the study were discussed.

• ETRI Journal
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• v.35 no.5
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• pp.849-858
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• 2013

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non-maneuvering and maneuvering modes, and, unlike the traditional decision-based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.

• Proceedings of the Korean Environmental Health Society Conference
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• 2005.11a
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• pp.126-129
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• 2005

본 연구는 서울지역 초등학교 학생 4, 5, 6학년 남 ${\cdot}$ 녀 초등학생들을 대상으로 봄철 대기오염농도와 일별 자각증상 과의 관련성에 대한 패널연구를 수행하였다. 로지회귀분석방법 중 일반화 추정 방정식(generalized estimating equations:GEE)모델을 이용하여 통계분석을 실시한 결과로는 서울지역 초등학교 학생들은 호흡기 증상보다는 피부가려움이나 눈의 따가움 등의 자극증상에 호흡성먼지(PM10)와 아황산가스 그리고 이산화질소 등의 대기오염물질 농도와 관련성이 있는 것으로 추정되었다.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.73-86
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• 2001

In this paper, we conducted an empirical study to investigate the design and weighting effects on descriptive and analytic statistics. We compared the regression models using the design-based approach and the generalized estimating equations(GEEs) approach with the model-based approach through the design and weighting effects analysis.

• Journal of Preventive Medicine and Public Health
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• v.50 no.6
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• pp.377-385
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• 2017

Objectives: Although mercury (Hg) exposure is known to be neurotoxic in humans, its effects on liver function have been less often reported. The aim of this study was to investigate whether total Hg exposure in Korean adults was associated with elevated serum levels of the liver enzymes aspartate aminotransferase (AST), alanine transaminase (ALT), and gamma-glutamyltransferase (GGT). Methods: We repeatedly examined the levels of total Hg and liver enzymes in the blood of 508 adults during 2010-2011 and 2014-2015. Cross-sectional associations between levels of blood Hg and liver enzymes were analyzed using a generalized linear model, and nonlinear relationships were analyzed using a generalized additive mixed model. Generalized estimating equations were applied to examine longitudinal associations, considering the correlations of individuals measured repeatedly. Results: GGT increased by 11.0% (95% confidence interval [CI], 4.5 to 18.0%) in women and 8.1% (95% CI, -0.5 to 17.4%) in men per doubling of Hg levels, but AST and ALT were not significantly associated with Hg in either men or women. In women who drank more than 2 or 3 times per week, AST, ALT, and GGT levels increased by 10.6% (95% CI, 4.2 to 17.5%), 7.7% (95% CI, 1.1 to 14.7%), and 37.5% (95% CI,15.2 to 64.3%) per doubling of Hg levels, respectively, showing an interaction between blood Hg levels and drinking. Conclusions: Hg exposure was associated with an elevated serum concentration of GGT. Especially in women who were frequent drinkers, AST, ALT, and GGT showed a significant increase, with a significant synergistic effect of Hg and alcohol consumption.

• Health Policy and Management
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• v.31 no.1
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• pp.125-139
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• 2021

Background: In Korea, the health gap widens due to the number of medical resources and access to medical services between metropolitan and rural. The purpose of this study is to identify the impact of residential migration on medical utilization and accessibility. Methods: This study extracted 528,516 claimed cases in the National Health Insurance Service-Cohort Sample Database from 2006 to 2015. Subjects were classified into two groups by the magnitude of the region, the metropolitan and the rural. The inversed probability weights were calculated for each group. And coefficients of the two-part model were estimated by generalized estimation equation. Results: Those who moved region from metropolitan to rural tend to increase the length of stay and inpatients with ambulatory care sensitive conditions (ACSC) disease. Contrariwise, those who moved areas from rural to metropolitan tend to decrease the total medical cost, the adjusted patient days, the number of outpatients and the number of outpatients and inpatients with ACSC disease. Conclusion: This study identified that between the residents who continued to reside in the region and the migrants, there were significant differences in the medical accessibility, quality of primary care, and unmet medical need.

• Structural Engineering and Mechanics
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• v.16 no.2
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• pp.219-240
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• 2003

One of the most versatile approaches for analyzing the dynamic behavior of structural systems is direct time integration of semi-discrete equations of motion. However responses computed by time integration are generally inexact and hence the corresponding errors would rather be studied in advance. In spite of the various error estimation formulations that exist in the literature, it is accepted practice to repeat the analyses with smaller time steps, followed by a comparison between the results. In this paper, after a review of this simple method and disregarding the round-off errors, a more efficient, reliable and yet simple method for estimating errors and enhancing the accuracy is proposed. The main objectives of this research are more realistic error estimation based on the concept of convergence, approximately controlling the reliability by comparing the actual rate of convergence with the integration method's order of accuracy, and enhancement of reliability by applying Richardson's extrapolation. Starting from the errors at specific time instants, the study is then generalized to cases in which the errors should be estimated and decreased at specific events e.g. peak responses. Numerical study illustrates the efficacy of the proposed method.