• Journal of Digital Convergence
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• v.14 no.12
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• pp.667-675
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• 2016

This study aims to investigate the effects of regular participation in physical activity on personal happiness by analyzing the "2014 National Leisure Sports Participation Survey". By using cross-tabulation and the binary logit model, it is found that there is a positive correlation between those two variables. The effect of positive affect on happiness index was found to be influenced by age, education level, occupation, income, marital status, appear. The results of the analysis of the relationship between the presence of sports facilities and the participation of regular physical activity using crosstab analysis and the analysis of physical activity showed positive relationship between two variables. Hence, it is expected that providing more opportunities to participate in sports programs can lead the public to more regular participation in physical activity.

• The Korean Journal of Community Living Science
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• v.24 no.1
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• pp.51-70
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• 2013

The purpose of the study was to analyze the determinants on the absence of after-school care among elementary students. This study is based on the National Children and Youth Panel Survey(2010) data and analyzed through Binary Logit Model and Multinominal Logit Model. The Following results were obtained: First, school grades, the number of siblings, mother's education, family type by parental employment, family structure, family type by parental nativity, and family income, all affected the absence of after-school care. Second, the absence days of after-school care was affected by different factors. 1day-2days a week in absence of after-school was more likely to increase among children with more siblings and an older father. On the other hand, spending over 3-4days a week without after-school care was more likely to increase among mothers with lower education, dual-earner families, multicultural families, lower family incomes, small cities and rural areas. Based on the results of this study, we agree with the generalization and the diversification of after-school care policy for elementary school students.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.534-540
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• 2020

Like most developed countries, South Korea is experiencing a severe lack of seafarers. Because young people's demands for a high quality of life has led them to seek shore-based careers. The wage difference between seafaring and shore-based careers has been decreasing gradually, however South Korea's unique Boarding Service Reserve System Policy has helped recruit and retain young sailors and, since 2017, the government has focused on creating new jobs and stabilizing the status of irregular workers in both private and public sectors. It specifically established the Economic, Social & Labor Council to carefully analyze seafarers' employment stability, which previously had been overlooked in the country. This research used the Binary Logit Model (BLM) to analyze the determinants of seafarers' employment stability in both permanent and non-permanent positions. We found that seafarers' employment stability correlated highly with their education level. This means that seafarers who graduated from the above mentioned two maritime universities would have more job stablity than those who graduated from maritime high schools or general universities. Other independent variables, such as the shipping company, vessel, hip management companies, work assignment, rank, and license had no significant impact on employment stability.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• Health Policy and Management
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• v.33 no.3
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• pp.295-310
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• 2023

Background: The long-term care (LTC) group has higher rates of chronic disease and disability registration compared to the general older people population. There is a need to provide integrated medical services and care for LTC group. Consequently, this study aimed to identify medical usage patterns based on the ratings of LTC and the characteristics of benefits usage in the LTC group. Methods: This study employed the National Health Insurance Service Database to analyze the effects of demographic and LTC-related characteristics on medical usage from 2015 to 2019 using a repeated measures analysis. A longitudinal logit model was applied to binary data, while a linear mixed model was utilized for continuous data. Results: In the case of LTC ratings, a positive correlation was observed with overall medical usage. In terms of LTC benefit usage characteristics, a higher overall level of medical usage was found in the group using home care benefits. Detailed analysis by medical institution classification revealed a maintained correlation between care ratings and the volume of medical usage. However, medical usage by classification varied based on the characteristics of LTC benefit usage. Conclusion: This study identified a complex interaction between LTC characteristics and medical usage. Predicting the requisite medical services based on the LTC rating presented a challenge. Consequently, it becomes essential for the LTC group to continuously monitor medical and care needs, even after admission into the LTC system. To facilitate this, it is crucial to devise an LTC rating system that accurately reflects medical needs and to broaden the implementation of integrated medical-care policies.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.265-274
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• 2017

For accurate demand forecasting of railway logistics, we estimated intercity freight mode choice models based on the binary logit model and using production-consumption data from the Korea Transport Database. We estimated two types of models and compared the results by major item of railway logistics, such as container, cement, and steel: 1) The aggregate freight mode choice models are based on the revealed preference (RP) data and 2) The disaggregate models are based on the stated preference (SP) data. With respect to the container, the travel time variable was found to be statistically significant; however, the travel cost variable was not statistically significant in the RP model, while the travel cost variable was statistically significant in the SP model. For cement and steel, the travel cost variables were statistically significant but the travel time variables were not statistically significant in either the RP or the SP models. These results are inconsistent with results from previous studies based on SP data, which showed that the travel time variables were significant. Consequently, it can be concluded that the travel time factor should be considered in container transport, but that this factor is negligible for cement and steel transport.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.791-799
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• 2008

To build the system which has high utilization and usefulness for users, it is necessary to know the information type and use-demand that the use want. The purpose of this study is to forecast the preference and demand of utilization for bus information when bus information is offered through cellular phon. The accomplishments of this research are as follow : Firstly, importance on the level of individual factor and the value of change's figure can be evaluated, using preference analysis on bus information by conjoint analysis. Secondly, by establishing the use-demand model bus information using binary logit model, influence factor on whether or not the use of the user. Finally, ordered probit model was built by use behavior model in payment per call or per month of potential user of bus information. Through call times and sensitive analysis by payment methods, elasticity point, optimal payment fee, and use probability was analyzed. This study make application as basic to efficient bus information policy and to improve use rate of bus information in future because this study make it possible to get preference analysis, use-demand analysis and estimation of optimal payment fee which is reflecting various requirement in use of bus information user.

• Journal of Families and Better Life
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• v.27 no.3
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• pp.31-43
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• 2009

Using the first wave of KLoSA(Korean Longitudinal Study of Ageing) beta version, this study analyzed factors affecting early retired men's subjective life satisfaction through Binary Logit and Multiple Regression. Total 552 men were selected as a sample. The main results of empirical analysis in this study were as follows: The retirement reason(health-) and monthly household income(+) affected whether they were satisfied with the retirement life or not and subjective life satisfaction over all. Especially, the retirement reason(health-) had a stronger effect on whether early retired min were satisfied with the retirement life or not and their subjective life satisfaction than monthly household income revealed significant variable in previous studies. This result represents that the retiree's life satisfaction analysis model must include retiree's characteristics at the time of retirement as well as retiree's current status characteristics or socio-economic characteristics.