• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• Economic and Environmental Geology
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• v.50 no.5
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• pp.363-373
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• 2017

In the present study, adaptability of cyber-physical system (CPS) for risk management of $CO_2$ storage site is examined and the subagging regression (SBR) method is proposed as a key component of the cyber-twin to estimate the risk due to potential $CO_2$ leakage. For these purposes, $CO_2$ concentration data monitored from a controlled $CO_2$ release field experiment is employed to validate the potentialities of the SBR method. From the validation study, it is found that the SBR method has robust estimation capability by showing minimal influence from anomalous measurements, and makes stable and sound predictions for the forthcoming $CO_2$ concentration trend. In addition, the method is found to be well suited as a tool of operational risk assessment based on real-time monitoring data due to the computational efficiency. The overall results suggest that the SBR method has potential to be an important component comprising the cyber twin of CPS for risk management of $CO_2$ storage site.

• The Journal of Information Technology
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• v.2 no.2
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• pp.251-267
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• 1999

This study examines that pricing multiple on and incremental explanatory power of equity book value(earnings) increase(decrease) as financial health decrease. Test using a sample of 75 bankrupt firms and test using a cross-sectional, pooled sample both yield inference consistent with predictions. It is thus hypothesized that the more bankrupt time are, the higher(lower) pricing multiple book value of equity(earnings) obtained. Findings are robust to inclusion of controls for debt/assets ratio, ROA, and ROIC. Overall, the results is the hypothesis.

• Journal of Korea Water Resources Association
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• v.32 no.4
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• pp.437-445
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• 1999

The present paper describes the problem of hydrologic response estimation using non-parametric ridge regression method. The method adapted in this work is based on the minimization of the $C_L$ statistics, which is an estimate of the mean square prediction error. For this method, effects of using both the identity matrix and the Laplacian matrix were considered. In addition, we evaluated methods for estimating the error variance of the impulse response. As a result of analyzing synthetic and real data, a good estimation was made when the Laplacian matrix for the weighting matrix and the bias corrected estimate for the error variance were used. The method and procedure presented in present paper will play a robust and effective role on separating hydrologic response.

• Environmental and Resource Economics Review
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• v.22 no.2
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• pp.215-250
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• 2013

The term, "resource curse", is widely used to describe how countries rich in natural resources, such as oil, natural gas, and certain minerals, are unable to utilize that wealth to boost their economies. Contrary to previous research on the topic, this study has demonstrated that natural resources have a strong positive correlation with a country's economy. It likewise confirmed that this result is robust with broad sets of exogenous variables, and that the positive impact of natural resources on the economy remains significant with the inclusion of capital stock per worker. In this sense, it is doubtful that resource curse actually exists in the long-run. On the other hand, this study tested whether the quality of institutions has any relation with natural resource endowments if the positive effect of natural resource endowments on the gross domestic product (GDP) is adequately controlled for. In contrast to findings of Alexeev and Conrad (2009), if the former Soviet Union (FSU) countries are included, it seems that there might be a negative and statistically significant relationship between large endowments of natural resources and the quality of institutions. However, this negative relationship loses its significance and some positive albeit insignificant relationships are confirmed in a considerable number of cases when the FSU countries are excluded in the sample. That is, the negative relationship results from the inclusion of the FSU countries. This result is believed to happen by a temporary coincidence of events, a natural resource windfall and political and economic instability during the transition of the FSU countries. Therefore, the argument that resource abundance harms the institutional quality is confirmed to be a little groundless.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2096-2109
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• 2012

This study investigates empirical issues that have received little attention in the previous research in the Korean capital market. It is to find any financial determinants on the capital structure for the firms listed in the KOSDAQ(Korea Securities Dealers Automated Quotation). Another test is performed to find any possible discriminating factors by utilizing a robust methodology, which may distinguish between the firms belonging the 'prime section' and the 'venture section' in terms of their financial aspects. Moreover, the null hypothesis that the changing trend or movement of a firm's capital structure with respect to its industry mean (or median) may be random, is also tested. For the book-value based debt ratios, size(INSIZE), growth(GROWTH), Market to book value of equity(MVBV), volatility(VOLATILITY), market value of equity (MVE) and section dummy (SECTION) showed their statistically significant effects on the book-value based leverage ratios, respectively, while size(INSIZE), growth(GROWTH), market value of equity(MVE), beta(BETA) and section dummy (SECTION) showed their statistically significant effects on the market-value based leverage ratios. This study also found an interesting result that a firm belonging to each corresponding industry has a tendency for reversion toward its mean and median leverage ratios over the five-year tested period.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.753-762
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• 2020

With an increasing interest in integrating artificial intelligence (AI) into interview processes, the Republic of Korea (ROK) army is trying to lead and analyze AI-powered interview platform. This study is to analyze the AI interview data using a unified non-crossing multiple quantile tree (UNQRT) model. Compared to the UNQRT, the existing models, such as quantile regression and quantile regression tree model (QRT), are inadequate for the analysis of AI interview data. Specially, the linearity assumption of the quantile regression is overly strong for the aforementioned application. While the QRT model seems to be applicable by relaxing the linearity assumption, it suffers from crossing problems among estimated quantile functions and leads to an uninterpretable model. The UNQRT circumvents the crossing problem of quantile functions by simultaneously estimating multiple quantile functions with a non-crossing constraint and is robust from extreme quantiles. Furthermore, the single tree construction from the UNQRT leads to an interpretable model compared to the QRT model. In this study, by using the UNQRT, we explored the relationship between the results of the Army AI interview system and the existing personnel data to derive meaningful results.

• 한국노년학
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• v.41 no.3
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• pp.295-308
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• 2021

This study is a descriptive study conducted to find out the predictive factors according to the level of the frailty of the communitydwelling older adult in a rural area. Data were collected from 400 older adults aged 65 years or older living in rural areas of Gyeongsangnam-do from October 2019 to March 2020. Data were analyzed using logistic regression to examine the predictive factors according to the level of frailty. The results showed that 27.8% for robust older adults, 30.9% for pre-frailty older adults, and 41.3% for frailty older adults. As a result of analyzing the predictive factors according to the level of frailty, the predictors from the robust stage to the pre-frailty stage were grip strength, nutritional status, and depression. The predictive factors for entering the pre-frailty stage into the frailty stage were gender, nutritional status, physical performance ability, and depression. Also, it was found that the predictive factors for entering from the robust stage to the frailty stage were sex, occupation, nutritional status, physical performance ability, and depression. Through this study, it was possible to understand the level of the frailty of the older adults living in rural communities and the effects of multidimensional variables. These results can be used as basic data necessary to find a way to prevent and manage the progression of frailty among older adults in rural areas.

• International Area Studies Review
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• v.21 no.4
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• pp.85-100
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• 2017

The study tries to shed empirical light on the relation between the concentration of population in the primary city and per capita economic growth of the country, using the data for 113 nations over the period, 2000-2010. The concentration of population is measured in two ways, the ratio of the primary city's population to the total and to the second city. Using the ratio of the primary city's population to that of the entire nation, the empirical results neither show the robust negative relations nor the reverse U relation between primary city's concentration and economic growth. The ratio of the primary city to the second city however turns out to have a negative relation to per capita GDP growth. This result implies economic growth of a nation can be enhanced by decreasing the gap between the primary and the second ranked cities and does not support the reverse U hypothesis by Handerson(1974, 2003).

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.