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Estimation of Log-odds Ratios for Incomplete 22 Tables with Covariates using FEFI
강신수,배제민 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.1
The information of covariates are available to do fully efficient fractional imputation(FEFI). The new method, FEFI with logistic regression is proposed to construct complete contingency tables. Jackknife method is used to get a standard errors of log-odds ratio from the completed table by the new method. Simulation results, when covariates have more information about categorical variables, reveal that the new method provides more efficient estimates of log-odds ratio than either multiple imputation(MI) based on data augmentation or complete case analysis.
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강신수,서운정,김명종 한국벤처창업학회 2020 벤처창업연구 Vol.15 No.1
There are common features among successful entrepreneurs without regarding to the stages of startup like challenge, growth, leap and maturity stages. We know that the successful entrepreneurs have strong entrepreneurship and interpersonal competency through many experiences in the real field of startup ecosystem. Entrepreneurship has a positive effect on intention to start a business, and the importance of entrepreneurship in business startups has been found through many studies. In addition, in most curriculum of startup, entrepreneurship education is operated like an introduction to the startup education. However, in research on entrepreneurship and education on entrepreneurship, it is not easy to find other factors related to interpersonal competence except for leadership. In this study, we redefined relation-building competences in consideration of relation-building parts such as self-openness, affinity, communication, empathy and consideration among various competencies of interpersonal relations, and analyzed the difference of startup intention according to the degree of relation-building competences. As a result, there was a significant difference in the degree of relation-building competences between the group of start-up entrepreneurs and the group with no intention of starting a business. It was proved that the relation-building competences is important for entrepreneurs. Therefore, in addition to studying entrepreneurship, which is represented by conductivity, innovation, autonomy, and risk sensitivity, the studying of relationship-building competences should be continued. It is expected that various research results analyzing the relationship between startups and relationship-building competences will be reflected in entrepreneurship education. 창업 도전기, 성장기, 도약기, 성숙기에 관계없이 성공한 창업가의 공통된 특징으로는 도전성, 혁신성, 자율성, 위험감수성 등과 같은 기업가정신뿐만 아니라 대인관계역량도 뛰어나다는 것을 경험적으로 인지하고 있다. 기업가정신이 창업의도에 긍정적인 영향을 미치며 창업에 있어서 기업가정신의 중요성 정도는 많은 연구를 통해 밝혀졌다. 또한 대부분 대학의 창업 교육과정에 있어서도 기업가정신 교육은 여타 학문의 개론과 같이 창업의 개론처럼 기업가정신 교과목이 운영 되고 있다. 그러나 대인관계역량과 관련된 요소를 찾기가 쉽지 않다. 본 연구에서는 대인관계의 여러 역량 중에 자기개방성, 친화성, 의사소통, 공감 및 배려와 같은 관계형성 부분을 고려하여 관계형성역량을 재 정의하고 관계형성역량 정도에 따라 창업의도 차이를 분석하였다. 그 결과 기 창업가와 창업의도가 전혀 없는 그룹간의 관계형성역량 정도가 유의하게 차이가 났으며 관계형성역량이 창업가에게 중요한 역량임을 증명하였다. 따라서 도전성, 혁신성, 자율성, 위험감수성 등으로 대표 되는 기업가정신 외에도 창업과 관계형성역량에 대한 연구가 계속 이어져야하며 창업과 관계형성역량과의 관계를 분석한 다양한 연구 결과가 창업 교육에도 반영되기를 기대한다.
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비선형 모형에 확장된 Longitudinal 자료 분석
姜信秀 관동대학교 1997 關大論文集 Vol.25 No.2
Liang & Zeger proposed an extension of generalized linear models to the analysis of longitudinal data. This paper shows that their suggestion can be extended to non-linear models. Asymptotic property is presented for the general class of estimators same as Liang & Zeger.
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Tests of independence in incomplete multi-way tables using likelihood functions
강신수,Michael D. Larsen 한국통계학회 2012 Journal of the Korean Statistical Society Vol.41 No.2
Kang (2006) and Kang and Larsen (in press) used the log likelihood function with Lagrangian multipliers for estimation of cell probabilities in two-way incomplete contingency tables. This paper extends results and simulations to three-way and multiway tables. Numerous studies cross-classify subjects by three or more categorical factors. Constraints on cell probabilities are incorporated through Lagrangian multipliers. Variances of the MLEs are derived from the matrix of second derivatives of the log likelihood with respect to cell probabilities and the Lagrange multiplier. Wald and likelihood ratio tests of independence are derived using the estimates and estimated variances. In simulation results in Kang and Larsen (in press), for data missing at random, maximum likelihood estimation (MLE) produced more efficient estimates of population proportions than either multiple imputation (MI) based on data augmentation or complete case (CC)analysis. Neither MLE nor MI, however, lead to an improvement over CC analysis with respect to power of tests for independence in two-way tables. Results are extended to multidimensional tables with arbitrary patterns of missing data when the variables are recorded on individual subjects. In three-way and higher-way tables, however, there is information relevant for judging independence in partially classified information, as long as two or more variables are jointly observed. Simulations study three-dimensional tables with three patterns of association and two levels of missing information.
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Large tests of independence in incomplete two-way contingency tables using fractional imputation
강신수 한국데이터정보과학회 2015 한국데이터정보과학회지 Vol.26 No.4
Imputation procedures fill-in missing values, thereby enabling complete data analyses. Fully effcient fractional imputation (FEFI) and multiple imputation (MI) create multiple versions of the missing observations, thereby re ecting uncertainty about their true values. Methods have been described for hypothesis testing with multiple imputation. Fractional imputation assigns weights to the observed data to compensate for missing values. The focus of this article is the development of tests of independence using FEFI for partially classified two-way contingency tables. Wald and deviance tests of independence under FEFI are proposed. Simulations are used to compare type I error rates and Power. The partially observed marginal information is useful for estimating the joint distribution of cell probabilities, but it is not useful for testing association. FEFI compares favorably to other methods in simulations.
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