• Journal of Digital Convergence
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• v.19 no.8
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• pp.21-32
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• 2021

The government is focusing its attention and support on start-ups. Nevertheless, there is anxiety and fear about starting a business at the base of public awareness. Experienced as a way to overcome fear and difficulty. Few prior studies have been done on experience factors as ones influencing entrepreneurial intention. In this study, I studied whether the experience of successful entrepreneurship through cultural indirect experience affect the resolution of fear about establishing a business. Among the influencing factors on the entrepreneurial intention, business motivation, social support and awareness of entrepreneur were selected as independent variables. In addition, by applying the cultivation theory, the drama role models were set as a controlling variable. For empirical analysis, a survey was conducted targeting 399 ordinary persons. The hypothesis was tested through regression analysis using the SPSS 23 statistical package. The moderating effect was analyzed using Process Macro 3.5. Self-fulfillment, livelihood, economic motivation, social support, and awareness of entrepreneur are sub-factors of business motivation, And all of these had a positive significant effect on entrepreneurial intention. Among the significant variables, self-fulfillment was found to have the greatest effect. On the other hand, as a result of analyzing the moderating effect of the drama role model, it was found play a role in controlling between self-fulfillment and entrepreneurial intention, between livelihood and entrepreneurial intention, and between awareness of entrepreneur and entrepreneurial intention. Based on these research results, academic and practical implications were presented.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.685-696
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• 2021

The Korean Economically Active Population Survey (KEAPS) is a national survey that produces employment-related statistics. The main purpose of the survey is to find out the economic activity status (employed/ unemployed/ non-employed) of the people. KEAPS has a unique characteristics caused by the survey method. In this study, through understanding of structural non-response and utilization of past data, we would like to present an improved imputation model. The performance of the proposed model is compared with the existing model through simulation. The performance of the imputation models is evaluated based on the degree of mathing/nonmatching rates. For this, we employ the KEAPS data in November 2019. For the randomly selected ones among the total 59,996 respondents, the six explanatory variables, which are critical in determining the economic activity states, are treated as non-response. The proposed model includes industry variable and job status variable in addition to the explanatory variables used in the precedent research. This is based on the linkage and utilization of past data. The simulation results confirm that the proposed model with additional variables outperforms the existing model in the precedent research. In addition, we consider various scenarios for the number of non-responders by the economic activity status.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.444-456
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• 2021

Economic abundance and the development of medical technology led to an aging society with an average life expectancy of 100 years, but retiring from the labor market at the age of 65 has become more difficult. This study aims to identify the influence of entrepreneurship, social support, and entrepreneurship mentoring as an effective support method to increase the entrepreneurial intention in order to enhance the entrepreneurial intention as an adult's second career development. In this study, data were collected using questionnaires from 340 adults, but only 319 were selected because 21 were judged to be inappropriate. For statistical analysis, SPSS 18.0 was used, and reliability test, factor analysis, and multiple regression analysis were used for hypothesis testing. The research results are as follows. First, as a result of examining the effects of adult entrepreneurship factors on entrepreneurship, it was found that among entrepreneurship, innovation and initiative had a significant positive (+) effect on entrepreneurship. Second, as a result of examining the effect of social support on entrepreneurial intention, it was found that family support had a significant negative (-) effect on entrepreneurial intention. Third, as a result of examining the effect of entrepreneurship mentoring on entrepreneurial intentions, it was found that role models and mentors had a positive (+) effect on entrepreneurial intentions. Fourth, as for the mediating effect of entrepreneurial efficacy, there were significant mediating effects of innovativeness → entrepreneurial efficacy → entrepreneurial intention, role model → entrepreneurial efficacy → entrepreneurial intention, mentor → entrepreneurial efficacy → entrepreneurial intention.

• Survey Research
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• v.18 no.4
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• pp.25-59
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• 2017

The current study investigated how elderly husbands and wives' marital satisfaction and depression change over time and examined how changes in marital satisfaction account for changes in depression between and within the dyads. The longitudinal dyadic data from the first wave(2006) to the fifth wave(2014) of the Korean Longitudinal Study of Aging(KLoSA), collected by the Korea Employment Information Service, were used for the analyses. Because husbands and wives are interdependent within couples, we applied statistical models for dyadic data. The dyadic growth model(DGM) was used to model the trajectories of marital satisfaction and depressive symptoms. In order to analyze the association of these growth factors, we proposed the dyadic growth actor-partner interdependence model(DG-APIM) and applied the model to the data. The results showed that on average the husbands' marital satisfaction was higher but decreased faster over the course of the study than the wives'. It also showed that the average depression of the husbands was lower than that of the wives but the husbands' depression increased faster than the wives' over the course of the study. The variance of the averages of husbands' (wives's) depression was accounted for by that of wives'(husbands') marital satisfaction, showing a partner effect. The variance of the slopes of husbands'(wives') depression was accounted for by that of marital satisfaction of themselves, showing an actor effect. The results showed that there is a longitudinal interdependence between husbands and wives' marital satisfaction and depression and supported the marital discord model of depression.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.139-149
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• 2008

The amount of personal information collected by organizations and government agencies is continuously increasing. When a data collector publishes personal information for research and other purposes, individuals' sensitive information should not be revealed. On the other hand, published data is also required to provide accurate statistical information for analysis. k-Anonymity and ${\iota}$-diversity models are popular approaches for privacy preserving data publication. However, they are limited to static data release. After a dataset is updated with insertions and deletions, a data collector cannot safely release up-to-date information. Recently, the m-invariance model has been proposed to support re-publication of dynamic datasets. However, the m-invariant generalization can cause high information loss. In addition, if the adversary already obtained sensitive values of some individuals before accessing released information, the m-invariance leads to severe privacy disclosure. In this paper, we propose a novel technique for safely releasing dynamic datasets. The proposed technique offers a simple and effective method for handling inserted and deleted records without generalization. It also gives equivalent degree of privacy preservation to the m-invariance model.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.46-54
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• 2021

This study compared and analyzed the effects of forest tending works on the vertical distribution of wildfire fuel loads on Pinus densiflora stands in Gyeongbuk province. The study sites were located in Youngju and Bonghwa in Pinus densiflora stands. A total of 10 sample trees were collected for the development of the crown fuel vertical distribution model. The 6th NFI (National Forest Inventory) selected a sample point that only extracted from managed and unmanaged stands of Pinus densiflora in the Gyeongbuk province. The fitness index (F.I.) of the two models developed was 0.984 to 0.989, with the estimated parameter showing statistical significance (P<0.05). A s a results, the vertical distribution of wildfire fuel loads range of unmanaged stands was from 1m to 11m with the largest distribution at point 5m at the tree height. On the other hand, the vertical distribution of wildfire fuel loads range of the managed stands was from 1m to 15m with the largest distribution at the point of 8m at the tree height. The canopy bulk density was 0.16kg/㎥ for the managed stands and 0.25kg/㎥ for the unmanaged stands, unmanaged stands were about 1.6 times more than managed stands. This result is expected to be available for simulation through the implementation of the 3D model as crown fuel was analyzed in three dimensions.

• 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.

• Journal of Chest Surgery
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• v.54 no.2
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• pp.88-98
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• 2021

Background: This study aimed to develop a new risk prediction model for operative mortality in a Korean cohort undergoing heart valve surgery using the Korea Heart Valve Surgery Registry (KHVSR) database. Methods: We analyzed data from 4,742 patients registered in the KHVSR who underwent heart valve surgery at 9 institutions between 2017 and 2018. A risk prediction model was developed for operative mortality, defined as death within 30 days after surgery or during the same hospitalization. A statistical model was generated with a scoring system by multiple logistic regression analyses. The performance of the model was evaluated by its discrimination and calibration abilities. Results: Operative mortality occurred in 142 patients. The final regression models identified 13 risk variables. The risk prediction model showed good discrimination, with a c-statistic of 0.805 and calibration with Hosmer-Lemeshow goodness-of-fit p-value of 0.630. The risk scores ranged from -1 to 15, and were associated with an increase in predicted mortality. The predicted mortality across the risk scores ranged from 0.3% to 80.6%. Conclusion: This risk prediction model using a scoring system specific to heart valve surgery was developed from the KHVSR database. The risk prediction model showed that operative mortality could be predicted well in a Korean cohort.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1119-1130
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• 2020

Flood disasters have been recently increasing worldwide due to climate change and extreme weather events. Since flood damage recovery has been conducted as a common coping strategy to flood disasters in the Republic of Korea, it is necessary to predict the regional flood damage costs by rainfall characteristics for a preventative measure to flood damage. Therefore, the purpose of this study is to present the regression functions for human and economic flood damage assessments for the 7 metropolises in the Republic of Korea. A comprehensive regression analysis was performed through the total 48 simple regression models on the two types of flood damage records for human and economic costs over the past two decades from 1998 to 2017 using the four kinds of nonlinear equations with each of the six rainfall variables. The damage assessment functions for each metropolis were finally selected by the evaluation of the regression results with the coefficient of determination and the statistical significance test, and then used for the human and economic flood damage assessments for 100-year rainfall in the 7 metropolises. The results of this study are expected to provide the basic information on flood damage cost assessments for flood damage mitigation measures.