• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.761-772
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• 2021

Simulation results for the comparison of estimators of interest are usually reported in tables or plots. However, if the simulations are conducted under various conditions for many estimators, the comparison can be difficult to be made with tables or plots. Furthermore, for algorithms that take a long time to run, the number of iterations of the simulation is costly to to be increased. The analysis of simulation results using regression models allows us to compare the estimators more systematically and effectively. Since variances in performance measures may vary depending on the simulation conditions and estimators, the heteroscedasticity of the error term should be allowed in the regression model. And multiple comparisons should be made because multiple estimators should be compared simultaneously. We introduce background theories of heteroscedasticity and multiple comparisons in the context of analyzing simulation results. We also present a concrete example.

• 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 Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.145-149
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• 2015

This paper introduces a sway suppression control for container cranes with unknown payloads and sway rates. With no priori knowledge concerning the magnitude of payload mass and sway rate, the proposed control maintains superior sway suppressing and trolley positioning against external disturbances. The proposed scheme combines a second order sliding mode control and an adaptive control to cope with unknown payloads. A second order sliding mode control without feedback of the sway rate is first designed, which is based on a class of feedback linearization methods for stabilization of the under-actuated sway dynamics of the container. Under applicable restrictions of the magnitude of payload inertia and sway rate, a linear regression model is obtained, and an adaptive control with a payload estimator is then designed, which is based on Lyapunov stability methods for the fast attenuation of trolley oscillations in the vicinity of the target position. The asymptotic stability of the overall closed-loop system is assured irrespective of variations of rope length. Simulation are shown in the existence of initial sway and external wind disturbances.

• Journal of Korean Academy of Nursing
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• v.34 no.6
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• pp.1025-1034
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• 2004

Purpose: This study defines a vulnerable group in a community that has become the main target of a national health project also, it is descriptive research to suggest an evidence-based direction to meet their deficit health-related needs, Method: This research examined 833 families and 1,835 family members of the financially vulnerable class that was registered in a home visiting program of a public health center. Among them, 892 persons who had health problems, and their family members were examined in detail to find out their characteristics of vulnerability and health needs by assessment during a nurses home visit. Frequency distribution, stepwise-regression and factor analysis were used to analyze the data. Result: The vulnerable group that was defined with social indexes set as standards, involved substantial characteristics of vulnerability. The characteristics of demand showed tendencies of being clustered in 5 factors needs of intensive nursing care, chronic nursing care problems and helplessness, maintenance of family functioning with a disability, deficient problem solving ability, and simple financial fragility. Conclusion: Categorization of needs is an evidence-based estimator of workload in nurse home visiting services, and can be used as a basic resource for direction to meet the deficit needs of a vulnerable group.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.305-317
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• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.538-550
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• 2012

This study suggests a general methodology which is designed for assessing RDOC behavior at the catchment scale by coupling properly a series of steam flow and water quality simulation models and actual monitoring data set. The modified TANK model in which a river routing function is incorporated to the conventional one is applied to simulate the long-term daily stream flow data, and the simulated stream flow data is combined with the 7-parameter log-linear model coupled to the minimum variance unbiased estimator to simulate the long-term daily water quality (BOD, COD and TOC) loads. Finally, the regression analysis between the usually monitored water quality data (BOD, COD and TOC) and RDOC is combined with the simulated water quality data to manifest the spatio-temporal variability of RDOC flux behavior at the Korean TMDL catchment scale.

• 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.

• Journal of Distribution Science
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• v.11 no.7
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• pp.5-13
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• 2013

Purpose - The study explores social well-being of the community of five selected countries of the South Asia: India, Pakistan, Sri Lanka, Nepal and Bangladesh. The study compares effectiveness of macroeconomic policies across the countries through interactive effects of the macroeconomic policy variables with the regional dummy variables. Research design, data, and methodology - Using the data set for the period of 1990-2008, this study employs panel data models, quantile regression methods, and the fixed effects method, which the constant is treated as group or country-specific. The model can also be known as the least-squares dummy variables estimator. Results - The results reveal significant chances of improvement in the well-being of the people while living in India and Pakistan as compared to the other countries of the region where India relatively stands with better chances of providing opportunities to improve the well-being of the people. Conclusions - This study recommends an increasing allocation of budget on education and health in order to enhance social well-being in the South Asian region. Inflation is the main cause of deteriorating well-being of the South Asian community by escalating the cost of living. Comprehensive study is recommended by employing the micro data models in the region.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.161-169
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• 2020

The condition of mining companies that exploit natural resources in their business processes underline this research to emphasize on social and environmental issues. After twelve years of government regulation on CSR practices, this study investigates the factors that influence mining companies in disclosing information about corporate social responsibility based on legitimacy, stakeholders, and agency theory. Thus, independent variables are foreign ownership, company size, leverage, and the board of commissioners. The dependent variable is the corporate social reporting disclosure that is measured using GRI indexing. For sampling, we have used thirty-four Indonesian mining companies listed in IDX during the 2014-2018. out of which only fifty-two companies meet the sample criteria. All data should pass the classical assumption test to get the best estimator. Multiple linear regression is used to test the hypothesis, and the results show that the model is good, and can explain 60% of the dependent variable. Based on F-test, all four variables affect CSR practices simultaneously. The findings of this study suggest that foreign ownership and firm size influences CSR disclosure in a positive direction. However, this study did not support the hypothesis that leverage negatively affects CSR disclosure and board size measures positively affect CSR disclosure.

• Journal of Applied Reliability
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• v.16 no.1
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• pp.56-63
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• 2016

Purpose: The purpose of this study is to point out that the Kaplan-Meier method is not valid to calculate the survival probability or failure probability (risk) in the presence of competing risks and to introduce more valid method of cumulative incidence function. Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly. Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced. Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.