• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.23-31
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• 2023

Predicting forest productivity is essential to evaluate sustainable forest management or to enhance forest ecosystem services. Ordinary least squares (OLS) and partial least squares (PLS) regression models were used to develop predictive models for forest productivity (site index) from the site characteristics and soil profile, along with soil physical and chemical properties, of 112 Quercus mongolica stands. The adjusted coefficients of determination (adjusted R²) in the regression models were higher for the site characteristics and soil profile of B horizon (R²=0.32) and of A horizon (R²=0.29) than for the soil physical and chemical properties of B horizon (R²=0.21) and A horizon (R²=0.09). The PLS models (R²=0.20-0.32) were better predictors of site index than the OLS models (R²=0.09-0.31). These results suggest that the regression models for Q. mongolica can be applied to predict the forest productivity, but new variables may need to be developed to enhance the explanatory power of regression models.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.3
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• pp.19-29
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• 2018

This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.8-14
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• 2009

When a farmer buys a tractor, the power size of a tractor is determined by various factors such as farm size, farmer's age, farming type, topographical area of farm. Relationships between tractor selection and these factors were found. Three regression models were developed to analyze the relationship. Those models were an OLS-1 model (based on 567 samples having tractors), an OLS-2 model, and a Tobit model (both based on the 1,941 samples). Regression analysis results showed that farm size and farmer's age affected selection of power size for all models at an 1% significance level. It was also shown that some farming types also had significant relationships with the tractor power size. Upland cultivating farmers and livestock farmers had larger tractors than rice cultivating farmers, while orchard farmers had smaller tractors. As for the topographical area, only middle area had significant difference with plain area. Farmers who had a rice-transplanter or a combine had larger tractors than those who didn't.

• 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.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.4
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• pp.458-471
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• 2019

DMSP-OLS nighttime satellite imagery could be used to derive the sum of lights (SOL) and built-up area, and the two indices have been widely employed to make the estimation of socio-economic variables and the dynamics of urban developments. Considering it, this research investigated the spatiotemporal patterns of economic growth and urbanized area in Jilin Province, China, using DMSP-OLS data for a time span between 1992 and 2012. This study found the SOLs of both the province and most cities to tend to grow during the period. While SOL-weighted centroids' means moved towards northwestern direction, urban-area centroids' means followed the trend of south-eastern migration. These directional patterns could be associated with the Northeast Revitalization Plan of Chinese governments. Nonetheless, a future study will need to consider SNPP VIIRS DNB imagery in order to overcome temporal limitation of DMSP-OLS data. In addition, it is also necessary to estimate socio-economic indices, e.g., growth regional domestic product, using a regression model developed with correlation relationship between economic statistics ad SOL.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1237-1246
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• 2021

The purpose of the research is to evaluate how education influences the income of household heads, who are young adult in rural Vietnam. In order to examine the impact of education on the households where their heads are young adults, in this paper, the authors employ two research methods. First, ordinary least squares (OLS) regression is used to study the impact of education on different groups of income; second, quantile regression is applied to find out how education influences the income of households. The dataset includes a survey of 800 young households aged between18 and 35 who are the head of agricultural farms in rural areas. The findings indicate that education has a positive impact on income of young households. Furthermore, the results prove that the longer schooling years, the higher income youth can attain. The results showed that, at the survey time (Sep 2019), the average monthly income of rural young adults who are joining the production process shows a big gap between low and high incomes. Moreover, the study has revealed that other factors positively affect the incomes, namely, joining job-related associations, land resource, hired labour, hi-tech application as well as extension of producing unit.

• New & Renewable Energy
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• v.17 no.3
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• pp.24-31
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• 2021

Through 'The Renewable Energy 3020 Implementation Plan' for carbon neutrality, the government promised to raise the proportion of renewable energy generation to 20% and renewable energy installation capacity to 63.8% by 2030. Therefore, we plan to test a 5.5 MW offshore wind turbine near Maldo, Gunsan. In this project, we measure the level of public acceptance and perform ordinary least squares (OLS) regression analysis to show the determinants of public acceptance. The regression results are as followed. First, it is judged that the closer the distance to the offshore wind turbine, the more the economic effects considered by residents. Second, especially in Maldo, the experience of being discriminated from the Saemangeum project, is understood to have caused distrust in the surrounding fishing villages chief/Fisheries Cooperatives, converted into a local community effect. Finally, the policy implications are as follows. First, a bottom-up problem-solving method is required to improve public acceptance, based on the Living Lab. Second, the island community may be indifferent to the briefings or forums of outsiders. Therefore, a gradual approach is required through (in)formal channels based on reliability from a long-term perspective with nearby universities and research institutes using SamsØ Energy Academy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3417-3426
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• 2012

This study aims to determine how ownership structure (share-holding ratio of insiders, foreigners) affects agent costs (the portion of asset efficiency or non-operating expenses) through empirical analysis. However, as existing studies on correlations between ownership structure and agent costs adopted Pooled OLS Model, this study focused on additionally formulating Fixed Effect Model and Random Effect Model aimed to reflect the time of data formation and corporate effects as study models based on verification results on the suitability of Pooled-OLS Model before comparative analysis for the purpose of improvement of credibility and statistical validity of the results of empirical analysis based on the premise that the Pooled OLS Model is not reliable enough to verify massive panel data. The data has been accumulated over 10 years from 1998 to 2007 after the IMF crisis hit the nation, from a subject 331 companies except for financial institutions. As a result of the empirical analysis, verification of the suitability of model has determined that the Random Effect Model is appropriate in terms of asset efficiency among agent costs items. On the other hand, the Fixed Effect Model is appropriate in terms of non-operating costs. As a result of the empirical analysis according to the appropriate model, no hypothesis adopted in the Pooled OLS Model has been accepted. This suggests that developing an appropriate model is more important than other factors for the purpose of generating statistically significant empirical results by showing that different empirical results are produced according to the type of empirical analysis.