• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.213-232
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• 2005

The purpose of this study is to determine factors affecting text difficulty and to model objective formulas which measure readability scores. Some readability-related factors such as total number of letters, total number of syllables, total number of unique syllables, total number of sentences and total number of paragraphs were found through correlation analysis. Some regression equations with these factors as their variables were produced through regression analysis. A model estimating readability score from total number of unique syllables was a good formula, while a model with two factors, total number of unique syllables and new syllable occurrence ratio, was a better enhanced one. The readability score represents detailed level so we can recommend students read texts corresponding to their reading levels.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.39-46
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• 2020

Recently, for designing virtual characters in the battle game field effectively, some methods are very needed to predicate the win rates of the battle of them efficiently. In this paper, we propose a method to solve this problem by combining simulation and machine learning. Firstly, a simulation is used to analyze the win rates of the battle of virtual characters in the battle game. In addition, we apply a regression model based machine learning scheme to predict win rates of the battle of virtual characters according to their abilities. Our experimental results using suggested method show that it is almost no difference between the win rates of the simulation and the prediction results using the machine learning scheme. And also, we can obtain good performance in the experiment using only simple regression based machine learning model.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.5-30
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• 2013

The aim of this study is to identify the influence of public housing on depression of its residents by analyzing the 7th Korea welfare panel study. To reduce the selection bias which arise from the public housing is selected by personal choice, we used PSM(propensity score matching). In addition, we merged the result of PSM and OLS regression to control the other variables which can affect depression of the resident of public housing. Final result revealed that the statistical significance which was observed when we compared the level of depression between the residents of public housing and general community by independent t-test was not observed when we used the merged result from PSM and regression. These results suggest that the high level of depression in the residents of public housing might be related with their demographic characteristics or earned income not with the public housing itself. This study can be the evidence supporting the policy of providing public housing because living in public housing did not give negative influence on its residents. Considering that this study also showed that there were no observable positive influences of public housing, we can suggest that public housing policy for majority of people not limited to people with low income.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.506-514
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• 2015

In this study, a new analytical techniques is proposed for seeking policy alternatives aimed at objectives of TDM, increasing the transit rideshare. Determinants of travel mode such as personal characteristics, lifestyle, and urban spatial characteristics are interdependent and have combined effect on decision. In addition, individuals, groups, and regional characteristics have interdependencies at different levels. Unlike traditional regression analysis, hierarchical analysis model has the advantage of identifying interdependencies and complex relationship between the combined impact factors. This analysis technique is expected to be a significant contribution to seek a more efficient TOD policy.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.163-170
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• 2003

This empirical study takes the activation approach for the competitive power of Korean shipping companies in the Korea-China liner routes. Data for this study were collected from Korea/ China/ 3rd flag shipping companies through the 500 questionnaires. The data of 250 respondents were analyzed statistically to verify the hypotheses and to induce Regression Equation which could predicts the influencing level of the determinants to competitive advantage for Korean shipping companies on Korea-China Liner Shipping Routes. Factor Analysis/ Cronbach's Alpha/ Principal Analysis/ Multiple Regression Analysis were used in order to test the hypotheses for the empirical study.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1389-1395
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• 2018

Hybrid is explained based on the logic of combination such as cross, melding, permeation, fusion, convergence etc. Such combination shows that it is more creative and effective in case of heterogeneity as compared to homogeneity and the same kind, and hybrid character has its meaning as a mean to produce a new creative image. In the context, this study aims to analyze influencing relationship through a practical analysis on how formative requirements for hybrid characters affects consumer preference. For the foregoing, this study conducted multiple regression analysis having familiarity, originality, meaningfulness, diversity as independent variables for formative requirements for characters, and consumer preference as a dependent variable. Analysis results show that familiarity, originality, diversity have a positive effect on consumers, whereas, meaningfulness has no significant impact on the consumer preference.

• Proceedings of the Korean DIstribution Association Conference
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• 2007.11a
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• pp.155-178
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• 2007

The purpose of this study is to provide the strategic implication of the Korean open air market by examining the factors affecting their competitiveness. I have undertaken empirical research that uses the methodology of a mixture regression modeling, as a way to ascertain the determinants of competitiveness for the Korean open air market. I construct a mixture regression model which uses the proportions of merchandise categories as explanatory variables and the number of visitors as a dependent variable. The analysis of results show that competitive and non-competitive markets have different proportions of merchandise categories. The finding shows that stock farm products and home appliances are major influencers on the number of visitors in neighborhood markets. The finding also presents that stock farm products and processed foods are major influencers on the number of visitors in small & medium-sized city markets.