• International Area Studies Review
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• v.20 no.2
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• pp.175-194
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• 2016

In this study, we aimed to identify the impact of socioeconomic characteristics on the health status of Chinese, which suggests that there might be the phenomena of health inequality in China. We used the year 2000, 2004 and 2009 pooled cross-sectional data of China Health and Nutrition Survey (CHNS), and utilized the Ordinary Least Square model (OLS) and Ordered Logit Estimation Method for this purpose. Empirical results showed that socioeconomic status and year dummy variables have a meaningful impact on Chinese health status. Therefore, we conclude that the phenomenon of health inequality has existed in China since 2000.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.1036-1045
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• 2013

The purpose of this study was to examine the effects of Simulation as a Problem-Based Learning (S-PBL) on critical thinking disposition, self-efficacy, and learning attitude and to compare an ordinary least square and a quantile regression method in impacting factors on critical thinking disposition. 143 students from six classes were randomly selected from a total of ten fundamental classes were assigned 66 in the control group and 77 in the experimental group. The results were that the experimental group received S-PBL and improved their critical thinking disposition and self-efficacy compared to the traditional learning method. In ordinary least square, affecting factors on critical thinking were the learning method and self-efficacy and these variables explained 41.0% in the critical thinking disposition. The results of the quantile regression method showed that affecting factors of critical thinking disposition were learning attitude of 0.1 quantile to 0.7 quantile and self-efficacy of all quantiles, and learning attitude of 0.4, 0.6, and 0.7 quantiles. Conclusion: The S-PBL is an effective method for nursing students who have low critical thinking disposition score to increase critical thinking disposition. And instructors can actively use S-PBL to enhance critical thinking disposition as well as self-efficacy in class.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.157-169
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• 2016

We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

• Analytical Science and Technology
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• v.16 no.3
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• pp.185-190
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• 2003

We applied modified least square method (MLS) and ordinary least square method (OLS) to 1st order equation for the comparison of the uncertainties calculated by these methods. The uncertainty calculated by OLS covered statistically safe interval because it was over-estimated in many cases of measurement and concentration level. But, if the uncertainty of the concentration as a reference value was comparably large (about 5% of the relative standard deviation of random scattering from the regression line and about 7% of relative standard uncertainty of reference values), then uncertainty calculated by OLS was seriously under-estimated at high concentration level. It was revealed that the calculated uncertainty didn't cover statistically safe interval at the stated confidence level. It was found that the method, MLS, described in the previously article would be valid for this calculation of uncertainty.

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.191-198
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• 1994

This paper deals with a feedback system for the estimation of geotechnical parameters in underground construction works. The Ordinary Least Square (OLS) Optimization Method is utilized and combined with Finite Element Program so that optimum values of ground properties can be estimated. The preperties that can be estimated are Young's and Brown's failure criteria is proposed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.677-687
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• 2006

Kriging interpolation is one of the generally used interpolation techniques in Geostatistics field. This technique includes the experimental and theoretical variograms and the formulation of kriging interpolation. In contrast to the conventional least square method for stress recovery, kriging interpolation is based on the weighted least square method to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by variogram modeling for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In addition to this, the p-level is increased non-uniformly or selectively through a posteriori error estimation based on SPR (superconvergent patch recovery) technique, proposed by Zienkiewicz and Zhu, by auto mesh p-refinement. The cut-out plate problem under tension has been tested to validate this approach. It also provides validity of kriging interpolation through comparing to existing least square method.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.943-958
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• 2008

This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.74-82
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• 2011

The Bass model is a cornerstone in diffusion theory which is used for forecasting demand of durables or new services. Three well-known estimation methods for parameters of the Bass model are Ordinary Least Square (OLS), Maximum Likelihood Estimator (MLE), Nonlinear Least Square (NLS). In this paper, a hybrid method incorporating OLS and NLS is presented and it's performance is analyzed and compared with OLS and NLS by using simulation data and empirical data. The results show that NLS has the best performance in terms of accuracy and our hybrid method has the best performance in terms of stability. Specifically, hybrid method has better performance with less data. This result means much in practical aspect because the avaliable data is little when a diffusion model is used for forecasting demand of a new product.

• The Journal of Economics, Marketing and Management
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• v.5 no.1
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• pp.27-37
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• 2017

Domestic government debt securities is one of the steps which is taken by the government of Indonesia as a major source of financial budget, covering for the budget deficit, debt payments and interest debt. The purposes of this research are to know the development of budget deficits, government debt and impact of domestic government debt securities against economic growth in Indonesia. Method of analysis used Ordinary Least Squares (OLS) analyzing the impact of the domestic debt against economic growth in Indonesia. This research uses time series data from 1997 to 2014. Total government debt and domestic government debt securities in Indonesia increased during the last five years. The average of domestic government securities was above 50 percent of the total government debt. Estimated results showed domestic government debt securities has a positive and significant effect to economic growth. Official development assistance (ODA) has a negative effect to economic growth. Other variables such as the gross fixed capital formation and receipt of remittance have positive and significant effect, total imports and government expenditure have negative and significant effect against economic growth.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.1-17
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• 2008

The ordinary least squares regression method of Haseman and Elston(1972) is most widely used in genetic linkage studies for continuous traits of sib pairs. Kruglyak and Lander(1995) suggested a statistic which appears to be a nonparametric counterpart to the Haseman and Elston(1972)'s regression method, but in fact these two methods are quite different. In this paper the relationships between these two methods are described and will be compared by simulation studies. One of the characteristics of the sib-pair linkage study is that the explanatory variable has only three different values and thus dependent variable is heavily replicated in each value of the explanatory variable. We propose a weighted least squares regression method which is more appropriate to this situation and the efficiency of the weighted regression in genetic linkage study was explored with normal and non-normal simulated continuous traits data. Simulation studies demonstrated that the weighted regression is more powerful than other tests.