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한국장학패널조사 자료를 활용한 청년층의 삶의 질에 미치는 영향 요인 분석
한준태,김은경,진태훈 한국데이터정보과학회 2023 한국데이터정보과학회지 Vol.34 No.6
In this paper, we analyzed the quality of life of young adults using the panel data from Korea Student Aid Panel Survey (2023). SAS 9.4 and Python 3.9 packages were used for statistical analysis and model construction, and values of variable affecting quality of life were checked. The weighted logistic regression analysis result showed that the major influencing factors for the quality of life of young adults are education level, career goal setting, major usefulness, loan balance, whether parents lived together, monthly household income, the rate of grant per tuition, the rate of unmet medical needs and the rate of social welfare budget. Also, as a result of AdaBoost algorithm, monthly income, monthly household income, education level, and rate of unmet medical needs were identified as relatively major variables in quality of life of young adults prediction model.
Estimation for Two-Parameter Rayleigh Distribution Based on Multiply Type-II Censored Sample
한준태,강석복 한국데이터정보과학회 2006 한국데이터정보과학회지 Vol.17 No.4
For multiply Type-II censored samples from two-parameter Rayleigh distribution, the maximum likelihood method does not admit explicit solutions. In this case, we propose some explicit estimators of the location and scale parameters in the Rayleigh distribution by the approximate maximum likelihood methods. We compare the proposed estimators in the sense of the mean squared error for various censored samples.
Estimation for the Half-Triangle Distribution Based on Progressively Type-II Censored Samples
한준태,강석복 한국데이터정보과학회 2008 한국데이터정보과학회지 Vol.19 No.3
We derive some approximate maximum likelihood estimators (AMLEs) and maximum likelihood estimator (MLE) of the scale parameter in the half-triangle distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.